Extracting high-fidelity RGBD information from two-dimensional (2D) images is essential for various visual computing applications. Stereo imaging, as a reliable passive imaging technique for obtaining three-dimensional (3D) scene information, has benefited greatly from deep learning advancements. However, existing stereo depth estimation algorithms struggle to perceive high-frequency information and resolve high-resolution depth maps in realistic camera settings with large depth variations. These algorithms commonly neglect the hardware parameter configuration, limiting the potential for achieving optimal solutions solely through software-based design strategies.This work presents a hardware-software co-designed RGBD imaging framework that leverages both stereo and focus cues to reconstruct texture-rich color images along with detailed depth maps over a wide depth range. A pair of rank-2 parameterized diffractive optical elements (DOEs) is employed to encode perpendicular complementary information optically during stereo acquisitions. Additionally, we employ an IGEV-UNet-fused neural network tailored to the proposed rank-2 encoding for stereo matching and image reconstruction. Through prototyping a stereo camera with customized DOEs, our deep stereo imaging paradigm has demonstrated superior performance over existing monocular and stereo imaging systems in both image PSNR by 2.96 dB gain and depth accuracy in high-frequency details across distances from 0.67 to 8 meters.

Digital imaging systems have traditionally relied on brute-force measurement and processing of pixels arranged on regular grids. In contrast, the human visual system performs significant data reduction from the large number of photoreceptors to the optic nerve, effectively encoding visual information into a low-bandwidth latent space representation optimized for brain processing. Inspired by this, we propose a similar approach to advance artificial vision systems.Latent Space Imaging introduces a new paradigm that combines optics and software to encode image information directly into the semantically rich latent space of a generative model. This approach substantially reduces bandwidth and memory demands during image capture and enables a range of downstream tasks focused on the latent space.We validate this principle through an initial hardware prototype based on a single-pixel camera. By implementing an amplitude modulation scheme that encodes into the generative model’s latent space, we achieve compression ratios ranging from 1:100 to 1:1000 during imaging, and up to 1:16384 for downstream applications. This approach leverages the model’s intrinsic linear boundaries, demonstrating the potential of latent space imaging for highly efficient imaging hardware, adaptable future applications in high-speed imaging, and task-specific cameras with significantly reduced hardware complexity.

Computer vision methods for depth estimation usually use simple camera models with idealized optics. For modern machine learning approaches, this creates an issue when attempting to train deep networks with simulated data, especially for focus-sensitive tasks like Depth-from-Focus. In this work, we investigate the domain gap caused by off-axis aberrations that will affect the decision of the best-focused frame in a focal stack. We then explore bridging this domain gap through aberration-aware training (AAT). Our approach involves a lightweight network that models lens aberrations at different positions and focus distances, which is then integrated into the conventional network training pipeline. We evaluate the generality of network models on both synthetic and real-world data. The experimental results demonstrate that the proposed AAT scheme can improve depth estimation accuracy without fine-tuning the model for different datasets.

Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to two-dimensional (2D) slices and require destructively slicing the object. Recovering the internal three-dimensional (3D) stress distribution of the entire object is challenging, as it involves solving a tensor tomography problem and handling phase wrapping ambiguities. We introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress tensor fields as neural implicit representations from polarization measurements. Our key insight is to jointly handle phase unwrapping and tensor tomography using a differentiable forward model based on Jones calculus. Our non-linear model faithfully matches real captures, unlike prior linear approximations. We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions. Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing photoelastic fringes from unseen viewpoints. NeST paves the way for scalable non-destructive 3D photoelastic analysis.

The desire for cameras with smaller form factors has recently led to a push for exploring computational imaging systems with reduced optical complexity such as a smaller number of lens elements. Unfortunately such simplified optical systems usually suffer from severe aberrations, especially in off-axis regions, which can be difficult to correct purely in software. In this paper we introduce Fovea Stacking, a new type of imaging system that utilizes an emerging dynamic optical component called the deformable phase plate (DPP) for localized aberration correction anywhere on the image sensor. By optimizing DPP deformations through a differentiable optical model, off-axis aberrations are corrected locally, producing a foveated image with enhanced sharpness at the fixation point - analogous to the eye's fovea. Stacking multiple such foveated images, each with a different fixation point, yields a composite image free from aberrations. To efficiently cover the entire field of view, we propose joint optimization of DPP deformations under imaging budget constraints. Due to the DPP device's non-linear behavior, we introduce a neural network-based control model for improved agreement between simulation and hardware performance. We further demonstrated that for extended depth-of-field imaging, Fovea Stacking outperforms traditional focus stacking in image quality. By integrating object detection or eye-tracking, the system can dynamically adjust the lens to track the object of interest-enabling real-time foveated video suitable for downstream applications such as surveillance or foveated virtual reality displays.

Modern nanofabrication techniques have enabled us to manipulate the wave-front of light with sub-wavelength-scale structures, offering the potential to replace bulky refractive surfaces in conventional optics with ultrathin metasurfaces. In theory, arrays of nanoposts provide unprecedented control over manipulating the wavefront in terms of phase, polarization, and amplitude at the nanometer resolution. A line of recent work successfully investigates flat computational cameras that replace compound lenses with a single metalens or an array of metasurfaces a few millimeters from the sensor. However, due to the inherent wavelength dependence of metalenses, in practice, these cameras do not match their refractive counterparts in image quality for broadband imaging, and may even suffer from hallucinations when relying on generative reconstruction methods. In this work, we investigate a collaborative array of metasurface elements that are jointly learned to perform broadband imaging. To this end, we learn a nanophotonics array with 100-million nanoposts that is end-to-end jointly optimized over the full visible spectrum—a design task that existing inverse design methods or learning approaches cannot support due to memory and compute limitations. We introduce a distributed meta-optics learning method to tackle this challenge. This allows us to optimize a large parameter array along with a learned metaatom proxy and a non-generative reconstruction method that is parallax-aware and noise-aware. The proposed camera performs favorably in simulation and in all experimental tests irrespective of the scene illumination spectrum.

Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made many innovative optical designs possible across a broad range of imaging and display applications. Many of these systems utilize diffractive optical components for holography, PSF engineering, or wavefront shaping. Existing approaches have, however, mostly remained limited to laboratory prototypes, owing to a large quality gap between simulation and manufactured devices. We aim at lifting the fundamental technical barriers to the practical use of learned diffractive optical systems. To this end, we propose a fabrication-aware design pipeline for diffractive optics fabricated by direct-write grayscale lithography followed by replication with nano-imprinting, which is directly suited for inexpensive mass-production of large area designs. We propose a super-resolved neural lithography model that can accurately predict the 3D geometry generated by the fabrication process. This model can be seamlessly integrated into existing differentiable optics frameworks, enabling fabrication-aware, end-to-end optimization of computational optical systems. To tackle the computational challenges, we also devise tensor-parallel compute framework centered on distributing large-scale FFT computation across many GPUs. As such, we demonstrate large scale diffractive optics designs up to 32.16 mm × 21.44 mm, simulated on grids of up to 128,640 by 85,760 feature points. We find adequate agreement between simulation and fabricated prototypes for applications such as holography and PSF engineering. We also achieve high image quality from an imaging system comprised only of a single diffractive optical element, with images processed only by a one-step inverse filter utilizing the simulation PSF. We believe our findings lift the fabrication limitations for real-world applications of diffractive optics and differentiable optical design.

Modeling surface reflectance is central to connecting optical theory with real-world rendering and fabrication. While analytic BRDFs remain standard in rendering, recent advances in geometric and wave optics have expanded the design space for complex reflectance effects. However, existing wave-optics-based methods are limited to controlling reflectance intensity only, lacking the ability to design full-spectrum, color-dependent BRDFs. In this work, we present the first method for designing and fabricating color BRDFs using a fully differentiable wave optics framework. Our differentiable and memory-efficient simulation framework supports end-to-end optimization of microstructured surfaces under scalar diffraction theory, enabling joint control over both angular intensity and spectral color of reflectance. We leverage grayscale lithography with a feature size of 1.5–2.0 μ m to fabricate 15 BRDFs spanning four representative categories: anti-mirrors, pictorial reflections, structural colors, and iridescences. Compared to prior work, our approach achieves significantly higher fidelity and broader design flexibility, producing physically accurate and visually compelling results. By providing a practical and extensible solution for full-color BRDF design and fabrication, our method opens up new opportunities in structural coloration, product design, security printing, and advanced manufacturing.

Welcome to the 16th IEEE International Conference on Computational Photography (ICCP 2024), taking place at the Ecole Polytéchnique Fédérale (EPFL) in Lausanne, Switzerland! This is the first time that ICCP takes place in Continental Europe, specifically on the shore of beautiful Lake Geneva. This year's two-and-a-half day conference features 18 accepted papers, 3 keynote talks, 8 invited talks, and 50 posters and/or demos.

While conventional cameras offer versatility for applications ranging from amateur photography to autonomous driving, computational cameras allow for domain-specific adaption. Cameras with co-designed optics and image processing algorithms enable high-dynamic-range image recovery, depth estimation, and hyperspectral imaging through optically encoding scene information that is otherwise undetected by conventional cameras. However, this optical encoding creates a challenging inverse reconstruction problem for conventional image recovery, and often lowers the overall photographic quality. Thus computational cameras with domain-specific optics have only been adopted in a few specialized applications where the captured information cannot be acquired in other ways. In this work, we investigate a method that combines two optical systems into one to tackle this challenge. We split the aperture of a conventional camera into two halves: one which applies an application-specific modulation to the incident light via a diffractive optical element to produce a coded image capture, and one which applies no modulation to produce a conventional image capture. Co-designing the phase modulation of the split aperture with a dual-pixel sensor allows us to simultaneously capture these coded and uncoded images without increasing physical or computational footprint. With an uncoded conventional image alongside the optically coded image in hand, we investigate image reconstruction methods that are conditioned on the conventional image, making it possible to eliminate artifacts and compute costs that existing methods struggle with. We assess the proposed method with 2-in-1 cameras for optical high-dynamic-range reconstruction, monocular depth estimation, and hyperspectral imaging, comparing favorably to all tested methods in all applications.

We present a novel framework for 3D tomographic reconstruction and visualization of tomograms from noisy electron microscopy tilt-series. Our technique takes as an input aligned tilt-series from cryogenic electron microscopy and creates denoised 3D tomograms using a proximal jointly-optimized approach that iteratively performs reconstruction and denoising, relieving the users of the need to select appropriate denoising algorithms in the pre-reconstruction or post-reconstruction steps. The whole process is accelerated by exploiting parallelism on modern GPUs, and the results can be visualized immediately after the reconstruction using volume rendering tools incorporated in the framework. We show that our technique can be used with multiple combinations of reconstruction algorithms and regularizers, thanks to the flexibility provided by proximal algorithms. Additionally, the reconstruction framework is open-source and can be easily extended with additional reconstruction and denoising methods. Furthermore, our approach enables visualization of reconstruction error throughout the iterative process within the reconstructed tomogram and on projection planes of the input tilt-series. We evaluate our approach in comparison with state-of-the-art approaches and additionally show how our error visualization can be used for reconstruction evaluation.

Particle imaging velocimetry is a classical method in 2D fluid imaging. While 3D extensions exist, they are limited by practical restrictions of multi-camera systems. Holographic particle imaging velocimetry has emerged as a solution for a simple and compact 3D imaging system. However, with dense particle seeding, scattering effects become apparent, and the reconstruction quality suffers, especially in the axial direction. To address these challenges, we propose a simple in-line HPIV approach with a plane-to-plane propagation model to account for the scattering effect. Instead of independently reconstructing particle volume and flow velocity, we present a joint optimization problem for particle and flow reconstruction. This optimization problem combines the a differentiable formulation of the holographic image formation with physical motion priors (incompressible flow and particle motion consistency) to improve the reconstruction quality. We solve this joint optimization problem using an extendable automatic differentiation and alternating optimization framework, and we evaluate the proposed method in synthetic and real experiments. The results demonstrate improved reconstruction quality for both particle density and flow velocity fields. With the plane-to-plane propagation model and physics prior, we push HPIV a step further regarding particle density, tank depth, and reconstruction accuracy.

We present a novel learning-based framework for reconstructing 3D structures from tilt-series cryo-Electron Tomography (cryo-ET) data. Cryo-ET is a powerful imaging technique that can achieve near-atomic resolutions. Still, it suffers from challenges such as missing-wedge acquisition, large data size, and high noise levels. Our framework addresses these challenges by using an adaptive tensorial-based representation for the 3D density field of the scanned sample. First, we optimize a quadtree structure to partition the volume of interest. Then, we learn a vector-matrix factorization of the tensor representing the density field in each node. Moreover, we use a loss function that combines a differentiable tomographic formation model with three regularization terms: total variation, boundary consistency constraint, and an isotropic Fourier prior. Our framework allows us to query the density at any location using the learned representation and obtain a high-quality 3D tomogram. We demonstrate the superiority of our framework over existing methods using synthetic and real data. Thus, our framework boosts the quality of the reconstruction while reducing the computation time and the memory footprint. The code is available at https://github.com/yuanhaowang1213/adaptivetensordf.

Projector Depth-of-Field (DOF) refers to the projection range of projector images in focus. It is a crucial property of projectors in spatial augmented reality (SAR) applications since wide projector DOF can increase the effective projection area on the projection surfaces with large depth variances and thus reduce the number of projectors required. Existing state-of-the-art methods attempt to create all-in-focus displays by adopting either a deep deblurring network or light modulation. Unlike previous work that considers the optimization of the deblurring model and physic modulation separately, in this paper, we propose an end-to-end joint optimization method to learn a diffractive optical element (DOE) placed in front of a projector lens and a compensation network for deblurring. Using the desired image and the captured projection result image, the compensation network can directly output the compensated image for display. We evaluate the proposed method in physical simulation and with a real experimental prototype, showing that the proposed method can extend the projector DOF by a minor modification to the projector and thus superior to the normal projection with a shallow DOF. The compensation method is also compared with the state-of-the-art methods and shows the advance in radiometric compensation in terms of computational efficiency and image quality.

Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that applies demosaicing (DM), denoising (DN), and super-resolution (SR) sequentially in a fixed and predefined pipeline (execution order of tasks), DM→DN→SR. The most recent work on image processing focuses on developing more sophisticated architectures to achieve higher image quality. Little attention has been paid to the design of the pipeline, and it is still not clear how significant the pipeline is to image quality. In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions. On the one hand, in sequential solutions, we find that the pipeline has a non-trivial effect on the resulted image quality. Our suggested pipeline DN→SR→DM yields consistently better performance than other sequential pipelines in various experimental settings and benchmarks. On the other hand, in joint solutions, we propose an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves the state-of-the-art performance for the mixture problem. We further present a novel and simple method that can integrate a certain pipeline into a given end-to-end network by providing intermediate supervision using a detachable head. Extensive experiments show that an end-to-end network with the proposed pipeline can attain only a consistent but insignificant improvement. Our work indicates that the investigation of pipelines is applicable in sequential solutions, but is not very necessary in end-to-end networks.

Tilt-series cryo-electron tomography (cryoET) is an established imaging technique used in several scientific fields to determine samples’ three-dimensional (3D) structures at nearatomic resolutions. However, the motion and misalignment that occur during the acquisition stage are major limiting factors to reaching smaller resolutions. Indeed, they introduce blur and artifacts, which deteriorate the reconstruction quality. In this paper, we propose a joint motion-correction and reconstruction framework to improve the quality of the output volume and, consequently, its resolution. Our framework first estimates the motion field in the sample in order to correct the captured data. Then an iterative plug-and-play prior approach is used to reconstruct the sample. The validation of our approach on real captured datasets shows a good quality reconstruction translated in a resolution improvement.

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster. https://github.com/darglein/NeAT

Unwanted camera obstruction can severely degrade captured images, including both scene occluders near the camera and partial occlusions of the camera cover glass. Such occlusions can cause catastrophic failures for various scene understanding tasks such as semantic segmentation, object detection, and depth estimation. Existing camera arrays capture multiple redundant views of a scene to see around thin occlusions. Such multi-camera systems effectively form a large synthetic aperture, which can suppress nearby occluders with a large defocus blur, but significantly increase the overall form factor of the imaging setup. In this work, we propose a monocular single-shot imaging approach that optically cloaks obstructions by emulating a large array. Instead of relying on different camera views, we learn a diffractive optical element (DOE) that performs depth-dependent optical encoding, scattering nearby occlusions while allowing paraxial wavefronts to be focused. We computationally reconstruct unobstructed images from these superposed measurements with a neural network that is trained jointly with the optical layer of the proposed imaging system. We assess the proposed method in simulation and with an experimental prototype, validating that the proposed computational camera is capable of recovering occluded scene information in the presence of severe camera obstruction.

We propose a new flash technique for low-light imaging, using deep-red light as an illuminating source. Our main observation is that in a dim environment, the human eye mainly uses rods for the perception of light, which are not sensitive to wavelengths longer than 620nm, yet the camera sensor still has a spectral response. We propose a novel modulation strategy when training a modern CNN model for guided image filtering, fusing a noisy RGB frame and a flash frame. This fusion network is further extended for video reconstruction. We have built a prototype with minor hardware adjustments and tested the new flash technique on a variety of static and dynamic scenes. The experimental results demonstrate that our method produces compelling reconstructions, even in extra dim conditions.

Imaging depth and spectrum have been extensively studied in isolation from each other for decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both information simultaneously by combining two different imaging systems; one for depth, the other for spectrum. While being accurate, this combinational approach induces increased form factor, cost, capture time, and alignment/registration problems. In this work, departing from the combinational principle, we propose a compact single-shot monocular HS-D imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. This enables us to reconstruct spectrum and depth from a single captured image. To this end, we develop a differentiable simulator and a neural-network-based reconstruction method that are jointly optimized via automatic differentiation. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager that acquires high-quality ground truth. We evaluate our method with synthetic and real experiments by building an experimental prototype and achieve state-of-the-art HS-D imaging results.

This paper examines the problem of illumination spectra estimation in multispectral images. We cast the problem into a constrained matrix factorization problem and present a method for both single-global and multiple illumination estimation in which a deep unrolling network is constructed from the alternating direction method of multipliers(ADMM) optimization for solving the matrix factorization problem. To alleviate the lack of multispectral training data, we build a large multispectral reflectance image dataset for generating synthesized data and use them for training and evaluating our model. The results of simulations and real experiments demonstrate that the proposed method is able to outperform state-of-the-art spectral illumination estimation methods, and that it generalizes well to a wide variety of scenes and spectra.

We present a method for reconstructing the 3D shape of underwater environments from a single, stationary camera placed above the water. We propose a novel differentiable framework, which, to our knowledge, is the first single-camera solution that is capable of simultaneously retrieving the structure of dynamic water surfaces and static underwater scene geometry in the wild. This framework integrates ray casting of Snell’s law at the refractive interface, multi-view triangulation and specially designed loss functions.Our method is calibration-free, and thus it is easy to collect data outdoors in uncontrolled environments. Experimental results show that our method is able to realize robust and quality reconstructions on a variety of scenes, both in a laboratory environment and in the wild, and even in a salt water environment. We believe the method is promising for applications in surveying and environmental monitoring.

We propose IntraTomo, a powerful framework that combines the benefits of learning-based and model-based approaches for solving highly ill-posed inverse problems in the Computed Tomography (CT) context. IntraTomo is composed of two core modules: a novel sinogram prediction module, and a geometry refinement module, which are applied iteratively. In the first module, the unknown density field is represented as a continuous and differentiable function, parameterized by a deep neural network. This network is learned, in a self-supervised fashion, from the incomplete or/and degraded input sinogram. After getting estimated through the sinogram prediction module, the density field is consistently refined in the second module using local and non-local geometrical priors. With these two core modules, we show that IntraTomo significantly outperforms existing approaches on several ill-posed inverse problems, such as limited angle tomography with a range of 45 degrees, sparse view tomographic reconstruction with as few as eight views, or super-resolution tomography with eight times increased resolution. The experiments on simulated and real data show that our approach can achieve results of unprecedented quality.

Abstract High dynamic range (HDR) image acquisition from a single image capture, also known as snapshot HDR imaging, is challenging because the bit depths of camera sensors are far from sufficient to cover the full dynamic range of the scene. Existing HDR techniques focus either on algorithmic reconstruction or hardware modification to extend the dynamic range. In this paper we propose a joint design for snapshot HDR imaging by devising a spatially varying modulation mask in the hardware and building a deep learning algorithm to reconstruct the HDR image. We leverage transfer learning to overcome the lack of sufficiently large HDR datasets available. We show how transferring from a different large‐scale task (image classification on ImageNet) leads to considerable improvements in HDR reconstruction. We achieve a reconfigurable HDR camera design that does not require custom sensors, and instead can be reconfigured between HDR and conventional mode with very simple calibration steps. We demonstrate that the proposed hardware–software so lution offers a flexible yet robust way to modulate per‐pixel exposures, and the network requires little knowledge of the hardware to faithfully reconstruct the HDR image. Comparison results show that our method outperforms the state of the art in terms of visual perception quality.

Abstract Division‐of‐focal‐plane (DoFP) polarization image sensors allow for snapshot imaging of linear polarization effects with inexpensive and straightforward setups. However, conventional interpolation based image reconstruction methods for such sensors produce unreliable and noisy estimates of quantities such as Degree of Linear Polarization (DoLP) or Angle of Linear Polarization (AoLP). In this paper, we propose a polarization demosaicking algorithm by inverting the polarization image formation model for both monochrome and colour DoFP cameras. Compared to previous interpolation methods, our approach can significantly reduce noise induced artefacts and drastically increase the accuracy in estimating polarization states. We evaluate and demonstrate the performance of the methods on a new high‐resolution colour polarization dataset. Simulation and experimental results show that the proposed reconstruction and analysis tools offer an effective solution to polarization imaging.

Imaging systems have long been designed in separated steps: experience-driven optical design followed by sophisticated image processing. Although recent advances in computational imaging aim to bridge the gap in an end-to-end fashion, the image formation models used in these approaches have been quite simplistic, built either on simple wave optics models such as Fourier transform, or on similar paraxial models. Such models only support the optimization of a single lens surface, which limits the achievable image quality. To overcome these challenges, we propose a general end-to-end complex lens design framework enabled by a differentiable ray tracing image formation model. Specifically, our model relies on the differentiable ray tracing rendering engine to render optical images in the full field by taking into account all on/off-axis aberrations governed by the theory of geometric optics. Our design pipeline can jointly optimize the lens module and the image reconstruction network for a specific imaging task. We demonstrate the effectiveness of the proposed method on two typical applications, including large field-of-view imaging and extended depth-of-field imaging. Both simulation and experimental results show superior image quality compared with conventional lens designs. Our framework offers a competitive alternative for the design of modern imaging systems.

High-dynamic range (HDR) imaging is an essential imaging modality for a wide range of applications in uncontrolled environments, including autonomous driving, robotics, and mobile phone cameras. However, existing HDR techniques in commodity devices struggle with dynamic scenes due to multi-shot acquisition and post-processing time, e.g. mobile phone burst photography, making such approaches unsuitable for real-time applications. In this work, we propose a method for snapshot HDR imaging by learning an optical HDR encoding in a single image which maps saturated highlights into neighboring unsaturated areas using a diffractive optical element (DOE). We propose a novel rank-1 parameterization of the proposed DOE which avoids vast trainable parameters and keeps high frequencies' encoding compared with conventional end-to-end design methods. We further propose a reconstruction network tailored to this rank-1 parametrization for recovery of clipped information from the encoded measurements. The proposed end-to-end framework is validated through simulation and real-world experiments and improves the PSNR by more than 7 dB over state-of-the-art end-to-end designs.

Visible light tomography is a promising and increasingly popular technique for fluid imaging. However, the use of a sparse number of viewpoints in the capturing setups makes the reconstruction of fluid flows very challenging. In this paper, we present a state-of-the-art 4D tomographic reconstruction framework that integrates several regularizers into a multi-scale matrix free optimization algorithm. In addition to existing regularizers, we propose two new regularizers for improved results: a regularizer based on view interpolation of projected images and a regularizer to encourage reprojection consistency. We demonstrate our method with extensive experiments on both simulated and real data.

Compressive imaging systems with spatial-temporal encoding can be used to capture and reconstruct fast-moving objects. The imaging quality highly depends on the choice of encoding masks and reconstruction methods. In this paper, we present a new network architecture to jointly design the encoding masks and the reconstruction method for compressive high-frame-rate imaging. Unlike previous works, the proposed method takes full advantage of denoising prior to provide a promising frame reconstruction. The network is also flexible enough to optimize full-resolution masks and efficient at reconstructing frames. To this end, we develop a new dense network architecture that embeds Anderson acceleration, known from numerical optimization, directly into the neural network architecture. Our experiments show the optimized masks and the dense accelerated network respectively achieve 1.5 dB and 1 dB improvements in PSNR without adding training parameters. The proposed method outperforms other state-of-the-art methods both in simulations and on real hardware. In addition, we set up a coded two-bucket camera for compressive high-frame-rate imaging, which is robust to imaging noise and provides promising results when recovering nearly 1,000 frames per second.

This paper addresses the problem of imaging in the presence of diffraction-photons. Diffraction-photons arise from the low contrast ratio of DMDs (∼1000:1), and very much degrade the quality of images captured by SPAD-based systems. Herein, a joint illumination-deconvolution scheme is designed to overcome diffraction-photons, enabling the acquisition of intensity and depth images. Additionally, a proof-of-concept experiment is conducted to demonstrate the viability of the designed scheme. It is shown that by co-designing the illumination and deconvolution phases of imaging, one can substantially overcome diffraction-photons.

Single Photon Avalanche Photodiodes (SPADs) have recently received a lot of attention in imaging and vision applications due to their excellent performance in low-light conditions, as well as their ultra-high temporal resolution. Unfortunately, like many evolving sensor technologies, image sensors built around SPAD technology currently suffer from a low pixel count. In this work, we investigate a simple, low-cost, and compact optical coding camera design that supports high-resolution image reconstructions from raw measurements with low pixel counts. We demonstrate this approach for regular intensity imaging, depth imaging, as well transient imaging. Our method uses an end-to-end framework to simultaneously optimize the optical design and a reconstruction network for obtaining super-resolved images from raw measurements. The optical design space is that of an engineered point spread function (implemented with diffractive optics), which can be considered an optimized anti-aliasing filter to preserve as much high-resolution information as possible despite imaging with a low pixel count, low fill-factor SPAD array. We further investigate a deep network for reconstruction. The effectiveness of this joint design and reconstruction approach is demonstrated for a range of different applications, including high-speed imaging, and time of flight depth imaging, as well as transient imaging. While our work specifically focuses on low-resolution SPAD sensors, similar approaches should prove effective for other emerging image sensor technologies with low pixel counts and low fill-factors.

Traditional snapshot hyperspectral imaging systems include various optical elements: a dispersive optical element (prism), a coded aperture, several relay lenses, and an imaging lens, resulting in an impractically large form factor. We seek an alternative, minimal form factor of snapshot spectral imaging based on recent advances in diffractive optical technology. We thereupon present a compact, diffraction-based snapshot hyperspectral imaging method, using only a novel diffractive optical element (DOE) in front of a conventional, bare image sensor. Our diffractive imaging method replaces the common optical elements in hyperspectral imaging with a single optical element. To this end, we tackle two main challenges: First, the traditional diffractive lenses are not suitable for color imaging under incoherent illumination due to severe chromatic aberration because the size of the point spread function (PSF) changes depending on the wavelength. By leveraging this wavelength-dependent property alternatively for hyperspectral imaging, we introduce a novel DOE design that generates an anisotropic shape of the spectrally-varying PSF. The PSF size remains virtually unchanged, but instead the PSF shape rotates as the wavelength of light changes. Second, since there is no dispersive element and no coded aperture mask, the ill-posedness of spectral reconstruction increases significantly. Thus, we propose an end-to-end network solution based on the unrolled architecture of an optimization procedure with a spatial-spectral prior, specifically designed for deconvolution-based spectral reconstruction. Finally, we demonstrate hyperspectral imaging with a fabricated DOE attached to a conventional DSLR sensor. Results show that our method compares well with other state-of-the-art hyperspectral imaging methods in terms of spectral accuracy and spatial resolution, while our compact, diffraction-based spectral imaging method uses only a single optical element on a bare image sensor.

Image segmentation is an important first step of many image processing, computer graphics, and computer vision pipelines. Unfortunately, it remains difficult to automatically and robustly segment cluttered scenes, or scenes in which multiple objects have similar color and texture. In these scenarios, light fields offer much richer cues that can be used efficiently to drastically improve the quality and robustness of segmentations. In this paper we introduce a new light field segmentation method that respects texture appearance, depth consistency, as well as occlusion, and creates well-shaped segments that are robust under view point changes. Furthermore, our segmentation is hierarchical, i.e. with a single optimization, a whole hierarchy of segmentations with different numbers of regions is available. All this is achieved with a submodular objective function that allows for efficient greedy optimization. Finally, we introduce a new tree-array type data structure, i.e. a disjoint tree, to efficiently perform submodular optimization on very large graphs. This approach is of interest beyond our specific application of light field segmentation. We demonstrate the efficacy of our method on a number of synthetic and real data sets, and show how the obtained segmentations can be used for applications in image processing and graphics.

Typical camera optics consist of a system of individual elements that are designed to compensate for the aberrations of a single lens. Recent computational cameras shift some of this correction task from the optics to post-capture processing, reducing the imaging optics to only a few optical elements. However, these systems only achieve reasonable image quality by limiting the field of view (FOV) to a few degrees - effectively ignoring severe off-axis aberrations with blur sizes of multiple hundred pixels. In this paper, we propose a lens design and learned reconstruction architecture that lift this limitation and provide an order of magnitude increase in field of view using only a single thin-plate lens element. Specifically, we design a lens to produce spatially shift-invariant point spread functions, over the full FOV, that are tailored to the proposed reconstruction architecture. We achieve this with a mixture PSF, consisting of a peak and and a low-pass component, which provides residual contrast instead of a small spot size as in traditional lens designs. To perform the reconstruction, we train a deep network on captured data from a display lab setup, eliminating the need for manual acquisition of training data in the field. We assess the proposed method in simulation and experimentally with a prototype camera system. We compare our system against existing single-element designs, including an aspherical lens and a pinhole, and we compare against a complex multielement lens, validating high-quality large field-of-view (i.e. 53°) imaging performance using only a single thin-plate element.

Computed tomography has emerged as the method of choice for scanning complex shapes as well as interior structures of stationary objects. Recent progress has also allowed the use of CT for analyzing deforming objects and dynamic phenomena, although the deformations have been constrained to be either slow or periodic motions. In this work we improve the tomographic reconstruction of time-varying geometries undergoing faster, non-periodic deformations. Our method uses a warp-and-project approach that allows us to introduce an essentially continuous time axis where consistency of the reconstructed shape with the projection images is enforced for the specific time and deformation state at which the image was captured. The method uses an efficient, time-adaptive solver that yields both the moving geometry as well as the deformation field. We validate our method with extensive experiments using both synthetic and real data from a range of different application scenarios.

We present an end-to-end image processing framework for time-of-flight (ToF) cameras. Existing ToF image processing pipelines consist of a sequence of operations including modulated exposures, denoising, phase unwrapping and multipath interference correction. While this cascaded modular design offers several benefits, such as closed-form solutions and power-efficient processing, it also suffers from error accumulation and information loss as each module can only observe the output from its direct predecessor, resulting in erroneous depth estimates. We depart from a conventional pipeline model and propose a deep convolutional neural network architecture that recovers scene depth directly from dual-frequency, raw ToF correlation measurements. To train this network, we simulate ToF images for a variety of scenes using a time-resolved renderer, devise depth-specific losses, and apply normalization and augmentation strategies to generalize this model to real captures. We demonstrate that the proposed network can efficiently exploit the spatio-temporal structures of ToF frequency measurements, and validate the performance of the joint multipath removal, denoising and phase unwrapping method on a wide range of challenging scenes.

Time-of-flight depth imaging and transient imaging are two imaging modalities that have recently received a lot of interest. Despite much research, existing hardware systems are limited either in terms of temporal resolution or are prohibitively expensive. Arrays of Single Photon Avalanche Diodes (SPADs) promise to fill this gap by providing higher temporal resolution at an affordable cost. Unfortunately SPAD arrays are to date only available in relatively small resolutions. In this work we aim to overcome the spatial resolution limit of SPAD arrays by employing a compressive sensing camera design. Using a DMD and custom optics, we achieve an image resolution of up to 800×400 on SPAD Arrays of resolution 64×32. Using our new data fitting model for the time histograms, we suppress the noise while abstracting the phase and amplitude information, so as to realize a temporal resolution of a few tens of picoseconds.

Depth-dependent defocus results in a limited depth-of-field in consumer-level cameras. Computational imaging provides alternative solutions to resolve all-in-focus images with the assistance of designed optics and algorithms. In this work, we extend the concept of focal sweep from refractive optics to diffractive optics, where we fuse multiple focal powers onto one single element. In contrast to state-of-the-art sweep models, ours can generate better-conditioned point spread function (PSF) distributions along the expected depth range with drastically shortened (40%) sweep distance. Further by encoding axially asymmetric PSFs subject to color channels, and then sharing sharp information across channels, we preserve details as well as color fidelity. We prototype two diffractive imaging systems that work in the monochromatic and RGB color domain. Experimental results indicate that the depth-of-field can be significantly extended with fewer artifacts remaining after the deconvolution.

In recent years, 3D Particle Imaging Velocimetry (PIV) has become more and more attractive due to its ability to fully characterize various fluid flows. However, 3D fluid capture and velocity field reconstruction remain a challenging problem. A recent rainbow PIV system encodes depth into color and successfully recovers 3D particle trajectories, but it also suffers from a limited and fixed volume size, as well as a relatively low light efficiency. In this paper, we propose a reconfigurable rainbow PIV system that extends the volume size to a considerable range. We introduce a parallel double-grating system to improve the light efficiency for scalable rainbow generation. A varifocal encoded diffractive lens is designed to accommodate the size of the rainbow illumination, ranging from 15 mm to 50 mm. We also propose a truncated consensus ADMM algorithm to efficiently reconstruct particle locations. Our algorithm is 5x faster compared to the state-of-the-art. The reconstruction quality is also improved significantly for a series of density levels. Our method is demonstrated by both simulation and experimental results.

Abstract We present a novel method to reconstruct a fluid's 3D density and motion based on just a single sequence of images. This is rendered possible by using powerful physical priors for this strongly under‐determined problem. More specifically, we propose a novel strategy to infer density updates strongly coupled to previous and current estimates of the flow motion. Additionally, we employ an accurate discretization and depth‐based regularizers to compute stable solutions. Using only one view for the reconstruction reduces the complexity of the capturing setup drastically and could even allow for online video databases or smart‐phone videos as inputs. The reconstructed 3D velocity can then be flexibly utilized, e.g., for re‐simulation, domain modification or guiding purposes. We will demonstrate the capacity of our method with a series of synthetic test cases and the reconstruction of real smoke plumes captured with a Raspberry Pi camera.

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing trade-off between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass discriminative training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.

Additive manufacturing has recently seen drastic improvements in resolution, making it now possible to fabricate features at scales of hundreds or even dozens of nanometers, which previously required very expensive lithographic methods. As a result, additive manufacturing now seems poised for optical applications, including those relevant to computer graphics, such as material design, as well as display and imaging applications. In this work, we explore the use of additive manufacturing for generating structural colors, where the structures are designed using a fabrication-aware optimization process. This requires a combination of full-wave simulation, a feasible parameterization of the design space, and a tailored optimization procedure. Many of these components should be re-usable for the design of other optical structures at this scale. We show initial results of material samples fabricated based on our designs. While these suffer from the prototype character of state-of-the-art fabrication hardware, we believe they clearly demonstrate the potential of additive nanofabrication for structural colors and other graphics applications.

In typical cameras the optical system is designed first; once it is fixed, the parameters in the image processing algorithm are tuned to get good image reproduction. In contrast to this sequential design approach, we consider joint optimization of an optical system (for example, the physical shape of the lens) together with the parameters of the reconstruction algorithm. We build a fully-differentiable simulation model that maps the true source image to the reconstructed one. The model includes diffractive light propagation, depth and wavelength-dependent effects, noise and nonlinearities, and the image post-processing. We jointly optimize the optical parameters and the image processing algorithm parameters so as to minimize the deviation between the true and reconstructed image, over a large set of images. We implement our joint optimization method using autodifferentiation to efficiently compute parameter gradients in a stochastic optimization algorithm. We demonstrate the efficacy of this approach by applying it to achromatic extended depth of field and snapshot super-resolution imaging.

Small unmanned aerial vehicles (UAVs) are ideal capturing devices for high-resolution urban 3D reconstructions using multi-view stereo. Nevertheless, practical considerations such as safety usually mean that access to the scan target is often only available for a short amount of time, especially in urban environments. It therefore becomes crucial to perform both view and path planning to minimize flight time while ensuring complete and accurate reconstructions. In this work, we address the challenge of automatic view and path planning for UAV-based aerial imaging with the goal of urban reconstruction from multi-view stereo. To this end, we develop a novel continuous optimization approach using heuristics for multi-view stereo reconstruction quality and apply it to the problem of path planning. Even for large scan areas, our method generates paths in only a few minutes, and is therefore ideally suited for deployment in the field. To evaluate our method, we introduce and describe a detailed benchmark dataset for UAV path planning in urban environments which can also be used to evaluate future research efforts on this topic. Using this dataset and both synthetic and real data, we demonstrate survey-grade urban reconstructions with ground resolutions of 1 cm or better on large areas (30 000 m 2 ).

Adaptive optics has become a valuable tool for correcting minor optical aberrations in applications such as astronomy and microscopy. However, due to the limited resolution of both the wavefront sensing and the wavefront correction hardware, it has so far not been feasible to use adaptive optics for correcting large-scale waveform deformations that occur naturally in regular photography and other imaging applications. In this work, we demonstrate an adaptive optics system for regular cameras. We achieve a significant improvement in focus for large wavefront distortions by improving upon a recently developed high resolution coded wavefront sensor, and combining it with a spatial phase modulator to create a megapixel adaptive optics system with unprecedented capability to sense and correct large distortions.

X-ray computed tomography (CT) is a valuable tool for analyzing objects with interesting internal structure or complex geometries that are not accessible with optical means. Unfortunately, tomographic reconstruction of complex shapes requires a multitude (often hundreds or thousands) of projections from different viewpoints. Such a large number of projections can only be acquired in a time-sequential fashion. This significantly limits the ability to use x-ray tomography for either objects that undergo uncontrolled shape change at the time scale of a scan, or else for analyzing dynamic phenomena, where the motion itself is under investigation. In this work, we present a non-parametric space-time tomographic method for tackling such dynamic settings. Through a combination of a new CT image acquisition strategy, a space-time tomographic image formation model, and an alternating, multi-scale solver, we achieve a general approach that can be used to analyze a wide range of dynamic phenomena. We demonstrate our method with extensive experiments on both real and simulated data.

Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on the alignment of nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task that requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high frame rate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules. In simplified optical designs, chromatic aberration can be one of the most significant causes for degraded image quality, and it can be quite difficult to remove in post-processing, since it results in strong blurs in at least some of the color channels. In this work, we revisit the pixel-wise similarity between different color channels of the image and accordingly propose a novel algorithm for correcting chromatic aberration based on this cross-channel correlation. In contrast to recent weak prior-based models, ours uses strong pixel-wise fitting and transfer, which lead to significant quality improvements for large chromatic aberrations. Experimental results on both synthetic and real world images captured by different optical systems demonstrate that the chromatic aberration can be significantly reduced using our approach.

Computational caustics and light steering displays offer a wide range of interesting applications, ranging from art works and architectural installations to energy efficient HDR projection. In this work we expand on this concept by encoding several target images into pairs of front and rear phase-distorting surfaces. Different target holograms can be decoded by mixing and matching different front and rear surfaces under specific geometric alignments. Our approach, which we call mix-and-match holography, is made possible by moving from a refractive caustic image formation process to a diffractive, holographic one. This provides the extra bandwidth that is required to multiplex several images into pairing surfaces. We derive a detailed image formation model for the setting of holographic projection displays, as well as a multiplexing method based on a combination of phase retrieval methods and complex matrix factorization. We demonstrate several application scenarios in both simulation and physical prototypes.

Despite significant recent progress, dense, time-resolved imaging of complex, non-stationary 3D flow velocities remains an elusive goal. In this work we tackle this problem by extending an established 2D method, Particle Imaging Velocimetry, to three dimensions by encoding depth into color. The encoding is achieved by illuminating the flow volume with a continuum of light planes (a "rainbow"), such that each depth corresponds to a specific wavelength of light. A diffractive component in the camera optics ensures that all planes are in focus simultaneously. With this setup, a single color camera is sufficient for tracking 3D trajectories of particles by combining 2D spatial and 1D color information. For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. We evaluate our method with both simulations and an experimental prototype setup.

We propose a concept for a lens attachment that turns a standard DSLR camera and lens into a light field camera. The attachment consists of eight low-resolution, low-quality side cameras arranged around the central high-quality SLR lens. Unlike most existing light field camera architectures, this design provides a high-quality 2D image mode, while simultaneously enabling a new high-quality light field mode with a large camera baseline but little added weight, cost, or bulk compared with the base DSLR camera. From an algorithmic point of view, the high-quality light field mode is made possible by a new light field super-resolution method that first improves the spatial resolution and image quality of the side cameras and then interpolates additional views as needed. At the heart of this process is a super-resolution method that we call iterative Patch- And Depth-based Synthesis (iPADS), which combines patch-based and depth-based synthesis in a novel fashion. Experimental results obtained for both real captured data and synthetic data confirm that our method achieves substantial improvements in super-resolution for side-view images as well as the high-quality and view-coherent rendering of dense and high-resolution light fields.

We propose a material classification method using raw time-of-flight (ToF) measurements. ToF cameras capture the correlation between a reference signal and the temporal response of material to incident illumination. Such measurements encode unique signatures of the material, i.e. the degree of subsurface scattering inside a volume. Subsequently, it offers an orthogonal domain of feature representation compared to conventional spatial and angular reflectance-based approaches. We demonstrate the effectiveness, robustness, and efficiency of our method through experiments and comparisons of real-world materials.

Cinema projectors need to compete with home theater displays in terms of image quality. High frame rate and spatial resolution as well as stereoscopic 3D are common features today, but even the most advanced cinema projectors lack in-scene contrast and, more important, high peak luminance, both of which are essential perceptual attributes of images appearing realistic. At the same time, HDR image statistics suggest that the average image intensity in a controlled ambient viewing environment such as the cinema can be as low as 1% for cinematic HDR content and not often higher than 18%, middle gray in photography. Traditional projection systems form images and colors by blocking the source light from a lamp, therefore attenuating between 99% and 82% of light, on average. This inefficient use of light poses significant challenges for achieving higher peak brightness levels. In this work, we propose a new projector architecture built around commercially available components, in which light can be steered to form images. The gain in system efficiency significantly reduces the total cost of ownership of a projector (fewer components and lower operating cost), and at the same time increases peak luminance and improves black level beyond what is practically achievable with incumbent projector technologies. At the heart of this computational display technology is a new projector hardware design using phase modulation in combination with a new optimization algorithm that is capable of on-the-fly computation of freeform lens surfaces.

Computational photography systems are becoming increasingly diverse, while computational resources---for example on mobile platforms---are rapidly increasing. As diverse as these camera systems may be, slightly different variants of the underlying image processing tasks, such as demosaicking, deconvolution, denoising, inpainting, image fusion, and alignment, are shared between all of these systems. Formal optimization methods have recently been demonstrated to achieve state-of-the-art quality for many of these applications. Unfortunately, different combinations of natural image priors and optimization algorithms may be optimal for different problems, and implementing and testing each combination is currently a time-consuming and error-prone process. ProxImaL is a domain-specific language and compiler for image optimization problems that makes it easy to experiment with different problem formulations and algorithm choices. The language uses proximal operators as the fundamental building blocks of a variety of linear and nonlinear image formation models and cost functions, advanced image priors, and noise models. The compiler intelligently chooses the best way to translate a problem formulation and choice of optimization algorithm into an efficient solver implementation. In applications to the image processing pipeline, deconvolution in the presence of Poisson-distributed shot noise, and burst denoising, we show that a few lines of ProxImaL code can generate highly efficient solvers that achieve state-of-the-art results. We also show applications to the nonlinear and nonconvex problem of phase retrieval.

Diffractive optical elements (DOEs) have recently drawn great attention in computational imaging because they can drastically reduce the size and weight of imaging devices compared to their refractive counterparts. However, the inherent strong dispersion is a tremendous obstacle that limits the use of DOEs in full spectrum imaging, causing unacceptable loss of color fidelity in the images. In particular, metamerism introduces a data dependency in the image blur, which has been neglected in computational imaging methods so far. We introduce both a diffractive achromat based on computational optimization, as well as a corresponding algorithm for correction of residual aberrations. Using this approach, we demonstrate high fidelity color diffractive-only imaging over the full visible spectrum. In the optical design, the height profile of a diffractive lens is optimized to balance the focusing contributions of different wavelengths for a specific focal length. The spectral point spread functions (PSFs) become nearly identical to each other, creating approximately spectrally invariant blur kernels. This property guarantees good color preservation in the captured image and facilitates the correction of residual aberrations in our fast two-step deconvolution without additional color priors. We demonstrate our design of diffractive achromat on a 0.5mm ultrathin substrate by photolithography techniques. Experimental results show that our achromatic diffractive lens produces high color fidelity and better image quality in the full visible spectrum.

Depth cameras are a ubiquitous technology used in a wide range of applications, including robotic and machine vision, human-computer interaction, autonomous vehicles as well as augmented and virtual reality. In this paper, we explore the design and applications of phased multi-camera time-of-flight (ToF) systems. We develop a reproducible hardware system that allows for the exposure times and waveforms of up to three cameras to be synchronized. Using this system, we analyze waveform interference between multiple light sources in ToF applications and propose simple solutions to this problem. Building on the concept of orthogonal frequency design, we demonstrate state-of-the-art results for instantaneous radial velocity capture via Doppler time-of-flight imaging and we explore new directions for optically probing global illumination, for example by de-scattering dynamic scenes and by non-line-of-sight motion detection via frequency gating.

Convolutional sparse coding (CSC) has become an increasingly important tool in machine learning and computer vision. Image features can be learned and subsequently used for classification and reconstruction tasks. As opposed to patch-based methods, convolutional sparse coding operates on whole images, thereby seamlessly capturing the correlation between local neighborhoods. In this paper, we propose a new approach to solving CSC problems and show that our method converges significantly faster and also finds better solutions than the state of the art. In addition, the proposed method is the first efficient approach to allow for proper boundary conditions to be imposed and it also supports feature learning from incomplete data as well as general reconstruction problems.

Although motion blur and rolling shutter deformations are closely coupled artifacts in images taken with CMOS image sensors, the two phenomena have so far mostly been treated separately, with deblurring algorithms being unable to handle rolling shutter wobble, and rolling shutter algorithms being incapable of dealing with motion blur. We propose an approach that delivers sharp and undistorted output given a single rolling shutter motion blurred image. The key to achieving this is a global modeling of the camera motion trajectory, which enables each scanline of the image to be deblurred with the corresponding motion segment. We show the results of the proposed framework through experiments on synthetic and real data.

Continuous-wave time-of-flight (ToF) cameras show great promise as low-cost depth image sensors in mobile applications. However, they also suffer from several challenges, including limited illumination intensity, which mandates the use of large numerical aperture lenses, and thus results in a shallow depth of field, making it difficult to capture scenes with large variations in depth. Another shortcoming is the limited spatial resolution of currently available ToF sensors. In this paper we analyze the image formation model for blurred ToF images. By directly working with raw sensor measurements but regularizing the recovered depth and amplitude images, we are able to simultaneously deblur and super-resolve the output of ToF cameras. Our method outperforms existing methods on both synthetic and real datasets. In the future our algorithm should extend easily to cameras that do not follow the cosine model of continuous-wave sensors, as well as to multi-frequency or multi-phase imaging employed in more recent ToF cameras.

Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can, therefore, only be obtained with the help of prior information in the form of (often nonconvex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with Peak Signal-to-Noise Ratio (PSNR) values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms.

Over the last few years, depth cameras have become increasingly popular for a range of applications, including human-computer interaction and gaming, augmented reality, machine vision, and medical imaging. Many of the commercially-available devices use the time-of-flight principle, where active illumination is temporally coded and analyzed in the camera to estimate a per-pixel depth map of the scene. In this paper, we propose a fundamentally new imaging modality for all time-of-flight (ToF) cameras: per-pixel radial velocity measurement. The proposed technique exploits the Doppler effect of objects in motion, which shifts the temporal illumination frequency before it reaches the camera. Using carefully coded illumination and modulation frequencies of the ToF camera, object velocities directly map to measured pixel intensities. We show that a slight modification of our imaging system allows for color, depth, and velocity information to be captured simultaneously. Combining the optical flow computed on the RGB frames with the measured metric radial velocity allows us to further estimate the full 3D metric velocity field of the scene. The proposed technique has applications in many computer graphics and vision problems, for example motion tracking, segmentation, recognition, and motion deblurring.

The functional difference between a diffuse wall and a mirror is well understood: one scatters back into all directions, and the other one preserves the directionality of reflected light. The temporal structure of the light, however, is left intact by both: assuming simple surface reflection, photons that arrive first are reflected first. In this paper, we exploit this insight to recover objects outside the line of sight from second-order diffuse reflections, effectively turning walls into mirrors. We formulate the reconstruction task as a linear inverse problem on the transient response of a scene, which we acquire using an affordable setup consisting of a modulated light source and a time-of-flight image sensor. By exploiting sparsity in the reconstruction domain, we achieve resolutions in the order of a few centimeters for object shape (depth and laterally) and albedo. Our method is robust to ambient light and works for large room-sized scenes. It is drastically faster and less expensive than previous approaches using femtosecond lasers and streak cameras, and does not require any moving parts.

We explore the connection between fluid capture, simulation and proximal methods, a class of algorithms commonly used for inverse problems in image processing and computer vision. Our key finding is that the proximal operator constraining fluid velocities to be divergence-free is directly equivalent to the pressure-projection methods commonly used in incompressible flow solvers. This observation lets us treat the inverse problem of fluid tracking as a constrained flow problem all while working in an efficient, modular framework. In addition it lets us tightly couple fluid simulation into flow tracking, providing a global prior that significantly increases tracking accuracy and temporal coherence as compared to previous techniques. We demonstrate how we can use these improved results for a variety of applications, such as re-simulation, detail enhancement, and domain modification. We furthermore give an outlook of the applications beyond fluid tracking that our proximal operator framework could enable by exploring the connection of deblurring and fluid guiding.

We analyze light propagation in an unknown scene using projectors and cameras that operate at transient timescales. In this new photography regime, the projector emits a spatio-temporal 3D signal and the camera receives a transformed version of it, determined by the set of all light transport paths through the scene and the time delays they induce. The underlying 3D-to-3D transformation encodes scene geometry and global transport in great detail, but individual transport components ( e.g ., direct reflections, inter-reflections, caustics, etc .) are coupled nontrivially in both space and time. To overcome this complexity, we observe that transient light transport is always separable in the temporal frequency domain . This makes it possible to analyze transient transport one temporal frequency at a time by trivially adapting techniques from conventional projector-to-camera transport. We use this idea in a prototype that offers three never-seen-before abilities: (1) acquiring time-of-flight depth images that are robust to general indirect transport, such as interreflections and caustics; (2) distinguishing between direct views of objects and their mirror reflection; and (3) using a photonic mixer device to capture sharp, evolving wavefronts of "light-in-flight".

We present a novel stochastic framework for non-blind deconvolution based on point samples obtained from random walks. Unlike previous methods that must be tailored to specific regularization strategies, the new Stochastic Deconvolution method allows arbitrary priors, including non-convex and data-dependent regularizers, to be introduced and tested with little effort. Stochastic Deconvolution is straightforward to implement, produces state-of-the-art results and directly leads to a natural boundary condition for image boundaries and saturated pixels.

Transient imaging is an exciting a new imaging modality that can be used to understand light propagation in complex environments, and to capture and analyze scene properties such as the shape of hidden objects or the reflectance properties of surfaces. Unfortunately, research in transient imaging has so far been hindered by the high cost of the required instrumentation, as well as the fragility and difficulty to operate and calibrate devices such as femtosecond lasers and streak cameras. In this paper, we explore the use of photonic mixer devices (PMD), commonly used in inexpensive time-of-flight cameras, as alternative instrumentation for transient imaging. We obtain a sequence of differently modulated images with a PMD sensor, impose a model for local light/object interaction, and use an optimization procedure to infer transient images given the measurements and model. The resulting method produces transient images at a cost several orders of magnitude below existing methods, while simultaneously simplifying and speeding up the capture process.

Modern imaging optics are highly complex systems consisting of up to two dozen individual optical elements. This complexity is required in order to compensate for the geometric and chromatic aberrations of a single lens, including geometric distortion, field curvature, wavelength-dependent blur, and color fringing. In this article, we propose a set of computational photography techniques that remove these artifacts, and thus allow for postcapture correction of images captured through uncompensated, simple optics which are lighter and significantly less expensive. Specifically, we estimate per-channel, spatially varying point spread functions, and perform nonblind deconvolution with a novel cross-channel term that is designed to specifically eliminate color fringing.

Recent years have seen proposals for exciting new computational display technologies that arecompressivein the sense that they generate high resolution images or light fields with relatively few display parameters. Image synthesis for these types of displays involves two major tasks: sampling and rendering high-dimensional target imagery, such as light fields or time-varying light fields, as well as optimizing the display parameters to provide a good approximation of the target content. In this paper, we introduce an adaptive optimization framework for compressive displays that generates high quality images and light fields using only a fraction of the total plenoptic samples. We demonstrate the framework for a large set of display technologies, including several types of auto-stereoscopic displays, high dynamic range displays, and high-resolution displays. We achieve significant performance gains, and in some cases are able to process data that would be infeasible with existing methods.

Abstract Simulation of light transport through lens systems plays an important role in graphics. While basic imaging properties can be conveniently derived from linear models (like ABCD matrices), these approximations fail to describe nonlinear effects and aberrations that arise in real optics. Such effects can be computed by proper ray tracing, for which, however, finding suitable sampling and filtering strategies is often not a trivial task. Inspired by aberration theory, which describes the deviation from the linear ray transfer in terms of wavefront distortions, we propose a ray‐space formulation for nonlinear effects. In particular, we approximate the analytical solution to the ray tracing problem by means of a Taylor expansion in the ray parameters. This representation enables a construction‐kit approach to complex optical systems in the spirit of matrix optics. It is also very simple to evaluate, which allows for efficient execution on CPU and GPU alike, including the computation of mixed derivatives of any order. We evaluate fidelity and performance of our polynomial model, and show applications in high‐quality offline rendering and at interactive frame rates.

Abstract Countershading is a common technique for local image contrast manipulations, and is widely used both in automatic settings, such as image sharpening and tonemapping, as well as under artistic control, such as in paintings and interactive image processing software. Unfortunately, countershading is a double‐edged sword: while correctly chosen parameters for a given viewing condition can significantly improve the image sharpness or trick the human visual system into perceiving a higher contrast than physically present in an image, wrong parameters, or different viewing conditions can result in objectionable halo artifacts. In this paper we investigate the perception of countershading in the context of a novel mask‐based contrast enhancement algorithm and analyze the circumstances under which the resulting profiles turn from image enhancement to artifact for a range of parameters and viewing conditions. Our experimental results can be modeled as a function of the width of the countershading profile. We employ this empirical function in a range of applications such as image resizing, view dependent tone mapping, and countershading analysis in photographs and works of fine art.

We present a novel approach for highly detailed 3D imaging of turbulent fluid mixing behaviors. The method is based on visible light computed tomography, and is made possible by a new stochastic tomographic reconstruction algorithm based on random walks. We show that this new stochastic algorithm is competitive with specialized tomography solvers such as SART, but can also easily include arbitrary convex regularizers that make it possible to obtain high-quality reconstructions with a very small number of views. Finally, we demonstrate that the same stochastic tomography approach can also be used to directly re-render arbitrary 2D projections without the need to ever store a 3D volume grid.

Without specialized sensor technology or custom, multi-chip cameras, high dynamic range imaging typically involves time-sequential capture of multiple photographs. The obvious downside to this approach is that it cannot easily be applied to images with moving objects, especially if the motions are complex. In this paper, we take a novel view of HDR capture, which is based on a computational photography approach. We propose to first optically encode both the low dynamic range portion of the scene and highlight information into a low dynamic range image that can be captured with a conventional image sensor. This step is achieved using a cross-screen, or star filter. Second, we decode, in software, both the low dynamic range image and the highlight information. Lastly, these two portions can be combined to form an image of a higher dynamic range than the regular sensor dynamic range.

We introduce a new approach to capturing refraction in transparent media, which we call Light Field Background Oriented Schlieren Photography (LFBOS). By optically coding the locations and directions of light rays emerging from a light field probe, we can capture changes of the refractive index field between the probe and a camera or an observer. Rather than using complicated and expensive optical setups as in traditional Schlieren photography we employ commodity hardware; our prototype consists of a camera and a lenslet array. By carefully encoding the color and intensity variations of a 4D probe instead of a diffuse 2D background, we avoid expensive computational processing of the captured data, which is necessary for Background Oriented Schlieren imaging (BOS). We analyze the benefits and limitations of our approach and discuss application scenarios.

Transformations between different color spaces and gamuts are ubiquitous operations performed on images. Often, these transformations involve information loss, for example when mapping from color to grayscale for printing, from multispectral or multiprimary data to tristimulus spaces, or from one color gamut to another. In all these applications, there exists a straightforward “natural” mapping from the source space to the target space, but the mapping is not bijective, resulting in information loss due to metamerism and similar effects. We propose a cluster-based approach for optimizing the transformation for individual images in a way that preserves as much of the information as possible from the source space while staying as faithful as possible to the natural mapping. Our approach can be applied to a host of color transformation problems including color to gray, gamut mapping, conversion of multispectral and multiprimary data to tristimulus colors, and image optimization for color deficient viewers.

Acquiring transparent, refractive objects is challenging as these kinds of objects can only be observed by analyzing the distortion of reference background patterns. We present a new, single image approach to reconstructing thin transparent surfaces, such as thin solids or surfaces of fluids. Our method is based on observing the distortion of light field background illumination. Light field probes have the potential to encode up to four dimensions in varying colors and intensities: spatial and angular variation on the probe surface; commonly employed reference patterns are only two-dimensional by coding either position or angle on the probe. We show that the additional information can be used to reconstruct refractive surface normals and a sparse set of control points from a single photograph.

Abstract Resizing to a lower resolution can alter the appearance of an image. In particular, downsampling an image causes blurred regions to appear sharper. It is useful at times to create a downsampled version of the image that gives the same impression as the original, such as for digital camera viewfinders. To understand the effect of blur on image appearance at different image sizes, we conduct a perceptual study examining how much blur must be present in a downsampled image to be perceived the same as the original. We find a complex, but mostly image‐independent relationship between matching blur levels in images at different resolutions. The relationship can be explained by a model of the blur magnitude analyzed as a function of spatial frequency. We incorporate this model in a new appearance‐preserving downsampling algorithm, which alters blur magnitude locally to create a smaller image that gives the best reproduction of the original image appearance.

Abstract The plenoptic function is a ray‐based model for light that includes the colour spectrum as well as spatial, temporal and directional variation. Although digital light sensors have greatly evolved in the last years, one fundamental limitation remains: all standard CCD and CMOS sensors integrate over the dimensions of the plenoptic function as they convert photons into electrons; in the process, all visual information is irreversibly lost, except for a two‐dimensional, spatially varying subset—the common photograph. In this state‐of‐the‐art report, we review approaches that optically encode the dimensions of the plenoptic function transcending those captured by traditional photography and reconstruct the recorded information computationally.

For backward compatible high dynamic range (HDR) video compression, the HDR sequence is reconstructed by inverse tone-mapping a compressed low dynamic range (LDR) version of the original HDR content. In this paper, we show that the appropriate choice of a tone-mapping operator (TMO) can significantly improve the reconstructed HDR quality. We develop a statistical model that approximates the distortion resulting from the combined processes of tone-mapping and compression. Using this model, we formulate a numerical optimization problem to find the tone-curve that minimizes the expected mean square error (MSE) in the reconstructed HDR sequence. We also develop a simplified model that reduces the computational complexity of the optimization problem to a closed-form solution. Performance evaluations show that the proposed methods provide superior performance in terms of HDR MSE and SSIM compared to existing tone-mapping schemes. It is also shown that the LDR image quality resulting from the proposed methods matches that produced by perceptually-based TMOs.

Interpolation between pairs of values, typically vectors, is a fundamental operation in many computer graphics applications. In some cases simple linear interpolation yields meaningful results without requiring domain knowledge. However, interpolation between pairs of distributions or pairs of functions often demands more care because features may exhibit translational motion between exemplars. This property is not captured by linear interpolation. This paper develops the use of displacement interpolation for this class of problem, which provides a generic method for interpolating between distributions or functions based on advection instead of blending. The functions can be non-uniformly sampled, high-dimensional, and defined on non-Euclidean manifolds, e.g., spheres and tori. Our method decomposes distributions or functions into sums of radial basis functions (RBFs). We solve a mass transport problem to pair the RBFs and apply partial transport to obtain the interpolated function. We describe practical methods for computing the RBF decomposition and solving the transport problem. We demonstrate the interpolation approach on synthetic examples, BRDFs, color distributions, environment maps, stipple patterns, and value functions.

We introduce polarization field displays as an optically-efficient design for dynamic light field display using multi-layered LCDs. Such displays consist of a stacked set of liquid crystal panels with a single pair of crossed linear polarizers. Each layer is modeled as a spatially-controllable polarization rotator, as opposed to a conventional spatial light modulator that directly attenuates light. Color display is achieved using field sequential color illumination with monochromatic LCDs, mitigating severe attenuation and moiré occurring with layered color filter arrays. We demonstrate such displays can be controlled, at interactive refresh rates, by adopting the SART algorithm to tomographically solve for the optimal spatially-varying polarization state rotations applied by each layer. We validate our design by constructing a prototype using modified off-the-shelf panels. We demonstrate interactive display using a GPU-based SART implementation supporting both polarization-based and attenuation-based architectures. Experiments characterize the accuracy of our image formation model, verifying polarization field displays achieve increased brightness, higher resolution, and extended depth of field, as compared to existing automultiscopic display methods for dual-layer and multi-layer LCDs.

Visual metrics can play an important role in the evaluation of novel lighting, rendering, and imaging algorithms. Unfortunately, current metrics only work well for narrow intensity ranges, and do not correlate well with experimental data outside these ranges. To address these issues, we propose a visual metric for predicting visibility (discrimination) and quality (mean-opinion-score). The metric is based on a new visual model for all luminance conditions, which has been derived from new contrast sensitivity measurements. The model is calibrated and validated against several contrast discrimination data sets, and image quality databases (LIVE and TID2008). The visibility metric is shown to provide much improved predictions as compared to the original HDR-VDP and VDP metrics, especially for low luminance conditions. The image quality predictions are comparable to or better than for the MS-SSIM, which is considered one of the most successful quality metrics. The code of the proposed metric is available on-line.

We develop tomographic techniques for image synthesis on displays composed of compact volumes of light-attenuating material. Such volumetric attenuators recreate a 4D light field or high-contrast 2D image when illuminated by a uniform backlight. Since arbitrary oblique views may be inconsistent with any single attenuator, iterative tomographic reconstruction minimizes the difference between the emitted and target light fields, subject to physical constraints on attenuation. As multi-layer generalizations of conventional parallax barriers, such displays are shown, both by theory and experiment, to exceed the performance of existing dual-layer architectures. For 3D display, spatial resolution, depth of field, and brightness are increased, compared to parallax barriers. For a plane at a fixed depth, our optimization also allows optimal construction of high dynamic range displays, confirming existing heuristics and providing the first extension to multiple, disjoint layers. We conclude by demonstrating the benefits and limitations of attenuation-based light field displays using an inexpensive fabrication method: separating multiple printed transparencies with acrylic sheets.

Multiplexing is a common technique for encoding high-dimensional image data into a single, two-dimensional image. Examples of spatial multiplexing include Bayer patterns to capture color channels, and integral images to encode light fields. In the Fourier domain, optical heterodyning has been used to acquire light fields. In this paper, we develop a general theory of multiplexing the dimensions of the plenoptic function onto an image sensor. Our theory enables a principled comparison of plenoptic multiplexing schemes, including noise analysis, as well as the development of a generic reconstruction algorithm. The framework also aides in the identification and optimization of novel multiplexed imaging applications.

Optically multiplexed image acquisition techniques have become increasingly popular for encoding different exposures, color channels, light fields, and other properties of light onto two-dimensional image sensors. Recently, Fourier-based multiplexing and reconstruction approaches have been introduced in order to achieve a superior light transmission of the employed modulators and better signal-to-noise characteristics of the reconstructed data. We show in this paper that Fourier-based reconstruction approaches suffer from severe artifacts in the case of sensor saturation, i.e. when the dynamic range of the scene exceeds the capabilities of the image sensor. We analyze the problem, and propose a novel combined optical light modulation and computational reconstruction method that not only suppresses such artifacts, but also allows us to recover a wider dynamic range than existing image-space multiplexing approaches.

In this paper, we propose a real-time tone-mapping scheme for backward compatible high dynamic range (HDR) video compression. The appropriate choice of a tone-mapping operator (TMO) can significantly improve the HDR quality reconstructed from a low dynamic range (LDR) version. We develop a statistical model that approximates the mean square error (MSE) distortion resulting from the combined processes of tone-mapping and compression. Using this model, we formulate a numerical optimization problem to find the tone-curve that minimizes the expected MSE in the reconstructed HDR sequence. We then simplify the developed model in order to reduce the computational complexity of the optimization problem to a closed-form solution. Performance evaluations show that the proposed methods provide superior performance in terms of HDR MSE and SSIM compared to existing tone-mapping schemes. It is also shown that the LDR image quality resulting from the proposed methods matches that produced by perceptually-based TMOs.

Abstract This state of the art report covers reconstruction methods for transparent and specular objects or phenomena. While the 3D acquisition of opaque surfaces with Lambertian reflectance is a well‐studied problem, transparent, refractive, specular and potentially dynamic scenes pose challenging problems for acquisition systems. This report reviews and categorizes the literature in this field. Despite tremendous interest in object digitization, the acquisition of digital models of transparent or specular objects is far from being a solved problem. On the other hand, real‐world data is in high demand for applications such as object modelling, preservation of historic artefacts and as input to data‐driven modelling techniques. With this report we aim at providing a reference for and an introduction to the field of transparent and specular object reconstruction. We describe acquisition approaches for different classes of objects. Transparent objects/phenomena that do not change the straight ray geometry can be found foremost in natural phenomena. Refraction effects are usually small and can be considered negligible for these objects. Phenomena as diverse as fire, smoke, and interstellar nebulae can be modelled using a straight ray model of image formation. Refractive and specular surfaces on the other hand change the straight rays into usually piecewise linear ray paths, adding additional complexity to the reconstruction problem. Translucent objects exhibit significant sub‐surface scattering effects rendering traditional acquisition approaches unstable. Different classes of techniques have been developed to deal with these problems and good reconstruction results can be achieved with current state‐of‐the‐art techniques. However, the approaches are still specialized and targeted at very specific object classes. We classify the existing literature and hope to provide an entry point to this exiting field.

Abstract Most objects deform gradually over time, without abrupt changes in geometry or topology, such as changes in genus. Correct space‐time reconstruction of such objects should satisfy this gradual change prior. This requirement necessitates a globally consistent interpretation of spatial adjacency. Consider the capture of a surface that comes in contact with itself during the deformation process, such as a hand with different fingers touching one another in parts of the sequence. Naive reconstruction would glue the contact regions together for the duration of each contact and keep them apart in other parts of the sequence. However such reconstruction violates the gradual change prior as it enforces a drastic intrinsic change in the object's geometry at the transition between the glued and unglued sub‐sequences. Instead consistent global reconstruction should keep the surfaces separate throughout the entire sequence. We introduce a new method for globally consistent space‐time geometry and motion reconstruction from video capture. We use the gradual change prior to resolve inconsistencies and faithfully reconstruct the geometry and motion of the scanned objects. In contrast to most previous methods our algorithm doesn't require a strong shape prior such as a template and provides better results than other template‐free approaches.

Abstract We propose to enhance the capabilities of the human visual system by performing optical image processing directly on an observed scene. Unlike previous work which additively superimposes imagery on a scene, or completely replaces scene imagery with a manipulated version, we perform all manipulation through the use of a light modulation display to spatially filter incoming light. We demonstrate a number of perceptually motivated algorithms including contrast enhancement and reduction, object highlighting for preattentive emphasis, colour saturation, de‐saturation and de‐metamerization, as well as visual enhancement for the colour blind. A camera observing the scene guides the algorithms for on‐the‐fly processing, enabling dynamic application scenarios such as monocular scopes, eyeglasses and windshields.

We introduce a purely passive facial capture approach that uses only an array of video cameras, but requires no template facial geometry, no special makeup or markers, and no active lighting. We obtain initial geometry using multi-view stereo, and then use a novel approach for automatically tracking texture detail across the frames. As a result, we obtain a high-resolution sequence of compatibly triangulated and parameterized meshes. The resulting sequence can be rendered with dynamically captured textures, while also consistently applying texture changes such as virtual makeup.

We demonstrate for the first time a proof of concept projector with a secondary array of individually controllable, analog micromirrors added to improve the contrast and peak brightness of conventional projectors. The micromirrors reallocate the light of the projector lamp from the dark parts towards the light parts of the image, before it reaches the primary image modulator. Each element of the analog micromirror array can be tipped/tilted to divert portions of the light from the lamp in two dimensions. By directing these mirrors on an image-dependent basis, we can increase both the peak intensity of the projected image as well as its contrast. In this paper, we describe and analyze the optical design for projectors using this light reallocation approach. We also discuss software algorithms to compute the best light reallocation pattern for a given input image, using the constraints of real hardware. We perform extensive simulations of this process to evaluate image quality and performance characteristics of this process. Finally, we present a first proof-of-concept implementation of this approach using a prototype analog micromirror device.

Abstract Tone mapping algorithms offer sophisticated methods for mapping a real‐world luminance range to the luminance range of the output medium but they often cause changes in color appearance. In this work we conduct a series of subjective appearance matching experiments to measure the change in image colorfulness after contrast compression and enhancement. The results indicate that the relation between contrast compression and the color saturation correction that matches color appearance is non‐linear and smaller color correction is required for small change of contrast. We demonstrate that the relation cannot be fully explained by color appearance models. We propose color correction formulas that can be used with existing tone mapping algorithms. We extend existing global and local tone mapping operators and show that the proposed color correction formulas can preserve original image colors after tone scale manipulation.

Abstract The presence of characteristic fine folds is important for modeling realistic looking virtual garments. While recent garment capture techniques are quite successful at capturing the low‐frequency garment shape and motion over time, they often fail to capture the numerous high‐frequency folds, reducing the realism of the reconstructed space‐time models. In our work we propose a method for reintroducing fine folds into the captured models using data‐driven dynamic wrinkling. We first estimate the shape and position of folds based on the original video footage used for capture and then wrinkle the surface based on those estimates using space‐time deformation. Both steps utilize the unique geometric characteristics of garments in general, and garment folds specifically, to facilitate the modeling of believable folds. We demonstrate the effectiveness of our wrinkling method on a variety of garments that have been captured using several recent techniques.

This paper presents a new algorithm for multi-view reconstruction that demonstrates both accuracy and efficiency. Our method is based on robust binocular stereo matching, followed by adaptive point-based filtering of the merged point clouds, and efficient, high-quality mesh generation. All aspects of our method are designed to be highly scalable with the number of views. Our technique produces the most accurate results among current algorithms for a sparse number of viewpoints according to the Middlebury datasets. Additionally, we prove to be the most efficient method among non-GPU algorithms for the same datasets. Finally, our scaled-window matching technique also excels at reconstructing deformable objects with high-curvature surfaces, which we demonstrate with a number of examples.

Fluid simulation is one of the most active research areas in computer graphics. However, it remains difficult to obtain measurements of real fluid flows for validation of the simulated data. In this paper, we take a step in the direction of capturing flow data for such purposes. Specifically, we present the first time-resolved Schlieren tomography system for capturing full 3D, non-stationary gas flows on a dense volumetric grid. Schlieren tomography uses 2D ray deflection measurements to reconstruct a time-varying grid of 3D refractive index values, which directly correspond to physical properties of the flow. We derive a new solution for this reconstruction problem that lends itself to efficient algorithms that robustly work with relatively small numbers of cameras. Our physical system is easy to set up, and consists of an array of relatively low cost rolling-shutter camcorders that are synchronized with a new approach. We demonstrate our method with real measurements, and analyze precision with synthetic data for which ground truth information is available.

A lot of research has recently focused on the problem of capturing the geometry and motion of garments. Such work usually relies on special markers printed on the fabric to establish temporally coherent correspondences between points on the garment's surface at different times. Unfortunately, this approach is tedious and prevents the capture of off-the-shelf clothing made from interesting fabrics. In this paper, we describe a marker-free approach to capturing garment motion that avoids these downsides. We establish temporally coherent parameterizations between incomplete geometries that we extract at each timestep with a multiview stereo algorithm. We then fill holes in the geometry using a template. This approach, for the first time, allows us to capture the geometry and motion of unpatterned, off-the-shelf garments made from a range of different fabrics.

Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel and fast approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a novel theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.

New generations of display devices promise to provide significantly improved dynamic range over conventional display technology. In the long run, evolving camera technology and file formats will provide high fidelity content for these display devices. In the near term, however, the vast majority of images and video will only be available in low dynamic range formats. In this paper we describe a method for boosting the dynamic range of legacy video and photographs for viewing on high dynamic range displays. Our emphasis is on real-time processing of video streams, such as web streams or the signal from a DVD player. We place particular emphasis on robustness of the method, and its ability to deal with a wide range of content without user adjusted parameters or visible artifacts. The method can be implemented on both graphics hardware and on signal processors that are directly integrated in the HDR displays.

Abstract Many algorithms in computer graphics improve their efficiency by using Hierarchical Space Subdivision Schemes (HS3), such as octrees, kD‐trees or BSP trees. Such HS3 usually provide an axis‐aligned subdivision of the 3D space embedding a scene or an object. However, the purely volume‐based behavior of these schemes often leads to strongly imbalanced surface clustering. In this article, we introduce the VS‐Tree, an alternative HS3 providing efficient and accurate surface‐based hierarchical clustering via a combination of a global 3D decomposition at coarse subdivision levels, and a local 2D decomposition at fine levels near the surface. First, we show how to efficiently construct VS‐Trees over meshes and point‐based surfaces, and analyze the improvement it offers for cluster‐based surface simplification methods. Then we propose a new surface reconstruction algorithm based on the volume‐surface classification of the VS‐Tree. This new algorithm is faster than state‐of‐the‐art reconstruction methods and provides a final semi‐regular mesh comparable to the output of remeshing algorithms.

We present a new algorithm for computing a polygonal surface from a union of balls. The method computes and connects the singular points of a given union of balls in an efficient manner to approximate the boundary. The algorithm uses the dual shape of the balls to give the resulting surface the correct topology. Our method is simple and demonstrated to be robust

Abstract We describe a system for transforming an off‐the‐shelf flatbed scanner into a $200 scan backend for large format cameras. While we describe both software and hardware aspects, the focus of the paper is on software issues such as color calibration and removal of scanner artifacts. With current scanner technology, the resulting camera system is capable of taking black&white, color, or near‐infrared photographs with up to 490 million pixels. Our analysis shows that we achieve actual optical resolutions close to the theoretical maximum, and that color reproduction is comparable to commercial camera systems. We believe that the camera system described here has many potential applications in image‐based modeling and rendering, cultural heritage projects, and professional digital photography.

The dynamic range of many real-world environments exceeds the capabilities of current display technology by several orders of magnitude. In this paper we discuss the design of two different display systems that are capable of displaying images with a dynamic range much more similar to that encountered in the real world. The first display system is based on a combination of an LCD panel and a DLP projector, and can be built from off-the-shelf components. While this design is feasible in a lab setting, the second display system, which relies on a custom-built LED panel instead of the projector, is more suitable for usual office workspaces and commercial applications. We describe the design of both systems as well as the software issues that arise. We also discuss the advantages and disadvantages of the two designs and potential applications for both systems.

Interactive visualization of complex, real-world light sources has so far not been feasible. We present a hardware accelerated direct lighting algorithm based on a recent high quality light source acquisition technique. By introducing an approximate reconstruction of the exact model, a multi-pass rendering approach, and a compact data representation, we are able to achieve interactive frame rates. The method is part of the processing pipeline from light source acquisition to high quality lighting of a virtual world.

The acquisition of geometric information from real-world objects has become a major way of modeling complex scenes and environments. Unfortunately, most optical methods for geometric model acquisition require the combination of partial information from different view points in order to obtain a single, coherent model. This, in turn, requires the registration of partial models into a common coordinate frame, a process that is usually done offline. As a consequence, holes due to undersampling and missing information often cannot be detected until after the registration. In this paper we introduce a fast, hardware-accelerated method for registering a new view to an existing partial geometric model in a volumetric representation. This method currently performs roughly one registration every second, and is therefore fast enough for on-the-fly evaluation by the user. Given more time, the same method is also capable of producing full geometric models at very high quality.

No abstract available.

We present a new algorithm for simplifying the shape of 3D objects by manipulating their medial axis transform (MAT). From an unorganized set of boundary points, our algorithm computes the MAT, decomposes the axis into parts, then selectively removes a subset of these parts in order to reduce the complexity of the overall shape. The result is simplified MAT that can be used for a variety of shape operations. In addition, a polygonal surface of the resulting shape can be directly generated from the filtered MAT using a robust surface reconstruction method. The algorithm presented is shown to have a number of advantages over other existing approaches.

Abstract Recent years have seen an increased interest in motion capture systems. Current systems, however, are limitedto only a few degrees of freedom, so that effectively only the motion of linked rigid bodies can be acquired. Wepresent a system for the capture of deformable surfaces, most notably moving cloth, including both geometry andparameterisation. We recover geometry using stereo correspondence, and use the Scale Invariant Feature Transform(SIFT) to identify an arbitrary pattern printed on the cloth, even in the presence of fast motion. We describea novel seed‐and‐grow approach to adapt the SIFT algorithm to deformable geometry. Finally, we interpolatefeature points to parameterise the complete geometry. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Physically based modelingI.4.8 [Image Processing and Computer Vision]: Scene analysis

Abstract In the last decade a new family of methods, namely Image‐Based Rendering, has appeared. These techniques rely on the use of precomputed images to totally or partially substitute the geometric representation of the scene. This allows to obtain realistic renderings even with modest resources. The main problem is the amount of data needed, mainly due to the high redundancy and the high computational cost of capture. In this paper we present a new method to automatically determine the correct camera placement positions in order to obtain a minimal set of views for Image‐Based Rendering. The input is a 3D polyhedral model including textures and the output is a set of views that sample all visible polygons at an appropriate rate. The viewpoints should cover all visible polygons with an adequate quality, so that we sample the polygons at sufficient rate. This permits to avoid the excessive redundancy of the data existing in several other approaches. We also reduce the cost of the capturing process, as the number of actually computed reference views decreases. The localization of interesting viewpoints is performed with the aid of an information theory‐based measure, dubbed viewpoint entropy. This measure is used to determine the amount of information seen from a viewpoint. Next we develop a greedy algorithm to minimize the number of images needed to represent a scene. In contrast to other approaches, our system uses a special preprocess for textures to avoid artifacts appearing in partially occluded textured polygons. Therefore no visible detail of these images is lost. ACM CSS: I.3.7 Computer Graphics—Three‐Dimensional Graphics and Realism

Realistic image synthesis requires both complex and realistic models of real-world light sources and efficient rendering algorithms to deal with them. In this paper, we describe a processing pipeline for dealing with complex light sources from acquisition to global illumination rendering. We carefully design optical filters to guarantee high precision measurements of real-world light sources. We discuss two practically feasible setups that allow us to measure light sources with different characteristics. Finally, we introduce an efficient importance sampling algorithm for our representation that can be used, for example, in conjunction with Photon Maps.

Real-world objects are usually composed of a number of different materials that often show subtle changes even within a single material. Photorealistic rendering of such objects requires accurate measurements of the reflection properties of each material, as well as the spatially varying effects. We present an image-based measuring method that robustly detects the different materials of real objects and fits an average bidirectional reflectance distribution function (BRDF) to each of them. In order to model local changes as well, we project the measured data for each surface point into a basis formed by the recovered BRDFs leading to a truly spatially varying BRDF representation. Real-world objects often also have fine geometric detail that is not represented in an acquired mesh. To increase the detail, we derive normal maps even for non-Lambertian surfaces using our measured BRDFs. A high quality model of a real object can be generated with relatively little input data. The generated model allows for rendering under arbitrary viewing and lighting conditions and realistically reproduces the appearance of the original object.

Many natural objects, and general layered materials, have non-linear reflection behaviour along wavelengths. An accurate representation of phenomena such as interference and colour separation generally requires a fine spectral representation of light instead of the commonly used RGB components. In this article, we introduce and experiment with a general approach to create similar and convincing effects, with a simple RGB BRDF model. We present a model for both specular and diffuse reflection.

Feathers, unlike other cutaneous appendages such as hair, fur, or scales have a definite structure. Variation in feather structure creates a wide range of resulting appearances. Collectively, feather structure determines the appearance of the feather coat, which can largely affect the resulting look of a feathered object (bird). In this paper we define the structure of individual feathers using a parameterization based on biological structure and substructures of actual feathers. We show that our parameterization can generate a large variety of feathers at multiple levels of detail and provide an initial step to semi-automatically generating a wide range of feather coats. his is achieved by specifying an intuitive interpolation between different structures and ages of feathers.

In recent years there has been a lot of work on interactively displaying global illumination solutions for non‐diffuse environments. This is an extremely active field of research, in which a lot of different approaches have recently been proposed. In this survey, we will discuss and compare these. This will hopefully prepare the ground for systematically addressing the open questions in the future.

A system is presented which automatically registers and stitches textures acquired from multiple photographic images onto the surface of a given corresponding 3D model. Within this process the camera position, direction and field of view must be determined for each of the images. For this registration, which aligns a 2D image to a 3D model we present an efficient hardware-accelerated silhouette-based algorithm working on different image resolutions that accurately registers each image without any user interaction. Besides the silhouettes, the given texture information can be used to improve accuracy by comparing one stitched texture to already registered images resulting in a global multi-view optimization. After the 3D-2D registration for each part of the 3D model's surface the view is determined which provides the best available texture. Textures are blended at the borders of regions assigned to different views.

Many researchers have been arguing that geometry, bump maps, and BRDFs present a hierarchy of detail that should be exploited for efficient rendering purposes. In practice however, this is often not possible due to inconsistencies in the illumination for these different levels of detail. For example, while bump map rendering often only considers direct illumination and no shadows, geometry-based rendering and BRDFs will mostly also respect shadowing effects, and in many cases even indirect illumination caused by scattered light.

With fast 3D graphics becoming more and more available even on low end platforms, the focus in developing new graphics hardware is beginning to shift towards higher quality rendering and additional functionality instead of simply higher performance implementations of the traditional graphics pipeline.On this search for improved quality it is important to identify a powerful set of orthogonal features to'be implemented in hardware, which can then be flexibly combined to form new algorithms.Pixel textures are an OpenGL extension by Silicon Graphics that fits into this category.In this paper, we demonstrate the benefits of this extension by presenting several different algorithms exploiting its functionality to achieve high quality, high performance solutions for a variety of different applications from scientific visualization and realistic image synthesis.We conclude that pixel textures are a valuable, powerful feature that should become a standard in future graphics systems.

With fast 3D graphics becoming more and more available even on low end platforms, the focus in hardware-accelerated rendering is beginning to shift towards higher quality rendering and additional functionality instead of simply higher performance implementations based on the traditional graphics pipeline. In this paper we present techniques for realistic shading and lighting using computer graphics hardware. In particular, we discuss multipass methods for high quality local illumination using physically-based reflection models, as well as techniques for the interactive visualization of non-diffuse global illumination solutions. These results are then combined with normal mapping for increasing the visual complexity of rendered images. Although the techniques presented in this paper work at interactive frame rates on contemporary graphics hardware, we also discuss some modifications of the rendering pipeline that help to further improve both performance and quality of the proposed met...

Light fields and Lumigraphs are capable of rendering scenes of arbitrary geometrical or illumination complexity in real time. They are thus interesting ways of interacting with both recorded real‐world and high‐quality synthetic scenes. Unfortunately, both light fields and Lumigraph rely on a dense sampling of the illumination to provide a good rendering quality. This induces high costs both in terms of storage requirements and computational resources for the image acquisition. Techniques for acquiring adaptive light field and Lumigraph representations are thus mandatory for practical applications. In this paper we present a method for the adaptive acquisition of images for Lumigraphs from synthetic scenes. Using image warping to predict the potential improvement in image quality when adding a certain view, we decide which new views of the scene should be rendered and added to the light field. This a‐priori error estimator accounts for both visibility problems and illumination effects such as specular highlights.

Procedural shaders have become popular tools for describing surface reflectance functions and other material properties. In comparison to fixed resolution textures, they have the advantage of being resolution-independent and storage-efficient. While procedural shaders provide an interface for evaluating the shader at a single point, it is not easily possible to obtain an average value of the shader together with accurate error bounds over a finite area. Yet the ability to compute such error bounds is crucial for several interesting applications, most notably heirarchical area sampling for global illumination, using the finite element approach, and for generation of textures used in interactive computer graphics. Using affine arithmetic for evaluating the shader over a finite area yields a tight, conservative error interval for the shader function. Compilers can automatically generate code for utilizing affine arithmetic from within shaders implemented in a dedicated language such as the RenderMann shading language.

Maximum projection is a volume rendering technique that, for each pixel, finds the maximum intensity along a projector. For certain important classes of data, this is an approximation to summation rendering which produces superior visualizations. We show how maximum projection rendering with additional depth cues can be implemented using simple affine transformations in object space. This technique can be used together with 3D graphics libraries and standard graphics hardware, thus allowing interactive manipulations of the volume data. The algorithm presented allows for a wide range of tradeoffs between interactivity and image quality.