We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Differentfrom the conventional coded-aperture imaging method, our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information, which is related to the differences in coding patterns, is recorded as events. The image frame and events, all of which are measured in a single exposure, are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website1:

A light field is usually represented as a set of multi-view images captured from a two-dimensional (2-D) array of viewpoints and requires a large amount of data compared with a standard 2-D image. We propose a 2-D compatible light-field compression method for encoding a light field as a 2-D monocular image and subsidiary data. In terms of the image quality, we prioritize the central image (regarded as the 2-D monocular image) over the other images in the light field, because the light field is considered an extension of the 2-D monocular image. To this end, we encode and decode the monocular image using a standard image codec and introduce a learned encoder and decoder pair for the subsidiary data. Experimental results indicate that our method achieved promising rate-distortion performance, especially for extremely low bit-rate ranges. Even though our method requires only a small amount of subsidiary data compared with those for the monocular image, the entire light field can be reconstructed with reasonable visual quality.

A light field is represented as a set of multi-view images captured from a dense 2-D array of viewpoints. To treat a light field as being continuous, we represent it as a neural radiance field (NeRF), which is a learned representation of a 3-D scene. NeRFs are renowned for their ability to reconstruct a target 3-D scene with compelling visual quality, but they are slow to train. A solution for this problem is to use a tiny neural network and trainable volumetric features as the scene representation, which is considered the baseline of our research. For further acceleration, we propose a method for warm-starting the per-scene training by setting good initial values for the trainable parameters. To this end, we introduce another encoder network to obtain the initial volumetric features from the target light field. Starting with the appropriate initial values, our method can achieve better rendering quality with fewer training iterations than the baseline.

We propose a method for compressively acquiring a dynamic light field (a 5-D volume) through a single-shot coded image (a 2-D measurement). We designed an imaging model that synchronously applies aperture coding and pixel-wise exposure coding within a single exposure time. This coding scheme enables us to effectively embed the original information into a single observed image. The observed image is then fed to a convolutional neural network (CNN) for light-field reconstruction, which is jointly trained with the camera-side coding patterns. We also developed a hardware prototype to capture a real 3-D scene moving over time. We succeeded in acquiring a dynamic light field with 5x5 viewpoints over 4 temporal sub-frames (100 views in total)from a single observed image. Repeating capture and reconstruction processes over time, we can acquire a dynamic light field at 4x the frame rate of the camera. To our knowledge, our method is the first to achieve a finer temporal resolution than the camera itself in compressive light-field acquisition. Our software is available from our project webpage.11https://www.fujii.nuee.nagoya-u.ac.jp/Research/CompCam2

Short-time discrete Fourier transform (ST-DFT) is known as a promising technique for image and video denoising. The seminal work by Saito and Komatsu hypothesized that natural video sequences can be represented by sparse ST-DFT coefficients and noisy video sequences can be denoised on the basis of statistical modeling and shrinkage of the ST-DFT coefficients. Motivated by their theory, we develop an application of ST-DFT for denoising multi-view images. We first show that multi-view images have sparse ST-DFT coefficients as well and then propose a new statistical model, which we call the multi-block Laplacian model, based on the block-wise sparsity of ST-DFT coefficients. We finally utilize this model to carry out denoising by solving a convex optimization problem, referred to as the least absolute shrinkage and selection operator. A closed-form solution can be computed by soft thresholding, and the optimal threshold value is derived by minimizing the error function in the ST-DFT domain. We demonstrate through experiments the effectiveness of our denoising method compared with several previous denoising techniques. Our method implemented in Python language is available from https://github.com/ctsutake/mviden.

Over the past decade, nonlinear image compression techniques based on neural networks have been rapidly developed to achieve more efficient storage and transmission of images compared with conventional linear techniques. A typical nonlinear technique is implemented as a neural network trained on a vast set of images, and the latent representation of a target image is transmitted. In contrast to the previous nonlinear techniques, we propose a new image compression method in which a neural network model is trained exclusively on a single target image, rather than a set of images. Such an overfitting strategy enables us to embed fine image features in not only the latent representation but also the network parameters, which helps reduce the reconstruction error against the target image. The effectiveness of our method is validated through a comparison with conventional image compression techniques in terms of a rate-distortion criterion.

A light field (LF), which is represented as a set of dense multiview images, has been utilized in various 3-D applications. To make LF acquisition more efficient, researchers have investigated compressive sensing methods by incorporating modulation or coding functions into the camera. In this work, we investigate a challenging case of compressive LF acquisition in which an entire LF should be reconstructed from only a single coded image. To achieve this goal, we propose a new modulation scheme called factorized modulation that can approximate arbitrary 4-D modulation patterns in a factorized manner. Our method can be hardware-implemented by combining the architectures for coded aperture and pixel-wise coded exposure imaging. The modulation pattern is jointly optimized with a CNN-based reconstruction algorithm. Our method is validated through extensive evaluations against other modulation schemes.

Compression of the sign information of discrete cosine transform coefficients is an intractable problem in image compression schemes due to the equiprobable occurrence of the sign bits. To overcome this difficulty, we propose an efficient compression method for such sign information based on phase retrieval, which is a classical signal restoration problem attempting to find the phase information of discrete Fourier transform coefficients from their magnitudes. In our compression strategy, the sign bits of all the AC components in the cosine domain are excluded from a bitstream at the encoder and are complemented at the decoder by solving a sign recovery problem, which we call sign retrieval. The experimental results demonstrate that the proposed method outperforms previous techniques for sign compression in terms of a rate-distortion criterion. Our method implemented in Python language is available from https://github.com/ctsutake/sr.

Coded exposure photography (CEP), originally proposed by Raskar et al., has been known as one of the promising techniques for motion deblurring. In this area, much efforts have been made for designing a fluttered shutter sequence to shape the spectrum of a uniformly motion-blurred image into an invertible one. Since the duty cycle of the fluttered shutters proposed thus far is generally low, the number of photons entering into an image sensor is reduced, which leads to a large Poisson noise in a low lighting condition. In the existing design techniques for the fluttered shutter, an increase of the duty cycle leads to a failure in the motion deblurring due to the singularities in the Fourier domain. To overcome the difficulty' this paper proposes a new motion deblurring framework using a higher duty-cycle fluttered shutter and a compressed sensing technique. The experimental results given in this paper demonstrate that the proposed technique is advantageous over a conventional one, in particular in a low lighting condition.