The restoration of a blurry or noisy image is commonly performed with a MAP estimator, which maximizes a posterior probability to reconstruct a clean image from a degraded image. A MAP estimator, when used with a sparse gradient image prior, reconstructs piecewise smooth images and typically removes textures that are important for visual realism. We present an alternative deconvolution method called iterative distribution reweighting (IDR) which imposes a global constraint on gradients so that a reconstructed image should have a gradient distribution similar to a reference distribution. In natural images, a reference distribution not only varies from one image to another, but also within an image depending on texture. We estimate a reference distribution directly from an input image for each texture segment. Our algorithm is able to restore rich mid-frequency textures. A large-scale user study supports the conclusion that our algorithm improves the visual realism of reconstructed images compared to those of MAP estimators.

Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel. Building upon this result, we describe two algorithms for estimating spatially invariant blur kernels. In the first method, we directly invert the transform, which is computationally efficient since it is not necessary to also estimate the latent sharp image. This approach is well suited for scenes with a diversity of edges, such as man-made environments. In the second method, we incorporate the Radon transform within the MAP estimation framework to jointly estimate the kernel and the image. While more expensive, this algorithm performs well on a broader variety of scenes, even when fewer edges can be observed. Our experiments show that our algorithms achieve comparable results to the state of the art in general and produce superior outputs on man-made scenes and photos degraded by a small kernel.

We explore the problem of reconstructing an image from a bag of square, non-overlapping image patches, the jigsaw puzzle problem. Completing jigsaw puzzles is challenging and requires expertise even for humans, and is known to be NP-complete. We depart from previous methods that treat the problem as a constraint satisfaction problem and develop a graphical model to solve it. Each patch location is a node and each patch is a label at nodes in the graph. A graphical model requires a pairwise compatibility term, which measures an affinity between two neighboring patches, and a local evidence term, which we lack. This paper discusses ways to obtain these terms for the jigsaw puzzle problem. We evaluate several patch compatibility metrics, including the natural image statistics measure, and experimentally show that the dissimilarity-based compatibility - measuring the sum-of-squared color difference along the abutting boundary - gives the best results. We compare two forms of local evidence for the graphical model: a sparse-and-accurate evidence and a dense-and-noisy evidence. We show that the sparse-and-accurate evidence, fixing as few as 4 - 6 patches at their correct locations, is enough to reconstruct images consisting of over 400 patches. To the best of our knowledge, this is the largest puzzle solved in the literature. We also show that one can coarsely estimate the low resolution image from a bag of patches, suggesting that a bag of image patches encodes some geometric information about the original image.

In image restoration tasks, a heavy-tailed gradient distribution of natural images has been extensively exploited as an image prior. Most image restoration algorithms impose a sparse gradient prior on the whole image, reconstructing an image with piecewise smooth characteristics. While the sparse gradient prior removes ringing and noise artifacts, it also tends to remove mid-frequency textures, degrading the visual quality. We can attribute such degradations to imposing an incorrect image prior. The gradient profile in fractal-like textures, such as trees, is close to a Gaussian distribution, and small gradients from such regions are severely penalized by the sparse gradient prior. To address this issue, we introduce an image restoration algorithm that adapts the image prior to the underlying texture. We adapt the prior to both low-level local structures as well as mid-level textural characteristics. Improvements in visual quality is demonstrated on deconvolution and denoising tasks.

The patch transform represents an image as a bag of overlapping patches sampled on a regular grid. This representation allows users to manipulate images in the patch domain, which then seeds the inverse patch transform to synthesize modified images. Possible modifications include the spatial locations of patches, the size of the output image, or the pool of patches from which an image is reconstructed. When no modifications are made, the inverse patch transform reduces to solving a jigsaw puzzle. The inverse patch transform is posed as a patch assignment problem on a Markov random field (MRF), where each patch should be used only once and neighboring patches should fit to form a plausible image. We find an approximate solution to the MRF using loopy belief propagation, introducing an approximation that encourages the solution to use each patch only once. The image reconstruction algorithm scales well with the total number of patches through label pruning. In addition, structural misalignment artifacts are suppressed through a patch jittering scheme that spatially jitters the assigned patches. We demonstrate the patch transform and its effectiveness on natural images.

We introduce the patch transform, where an image is broken into non-overlapping patches, and modifications or constraints are applied in the “patch domain”. A modified image is then reconstructed from the patches, subject to those constraints. When no constraints are given, the reconstruction problem reduces to solving a jigsaw puzzle. Constraints the user may specify include the spatial locations of patches, the size of the output image, or the pool of patches from which an image is reconstructed. We define terms in a Markov network to specify a good image reconstruction from patches: neighboring patches must fit to form a plausible image, and each patch should be used only once. We find an approximate solution to the Markov network using loopy belief propagation, introducing an approximation to handle the combinatorially difficult patch exclusion constraint. The resulting image reconstructions show the original image, modified to respect the user’s changes. We apply the patch transform to various image editing tasks and show that the algorithm performs well on real world images.

This paper presents a hybrid CNT/CMOS chemical sensor system that comprises of a carbon nanotube sensor array and a CMOS interface chip. The full system, including the sensor, consumes 32 muW at 1.83 kS/s readout rate, accomplished through an extensive use of CAD tools and a model-based architecture optimization. A redundant use of CNT sensors in the frontend increases the reliability of the system.

Object motion during camera exposure often leads to noticeable blurring artifacts. Proper elimination of this blur is challenging because the blur kernel is unknown, varies over the image as a function of object velocity, and destroys high frequencies. In the case of motions along a 1D direction (e.g. horizontal) we show that these challenges can be addressed using a camera that moves during the exposure. Through the analysis of motion blur as space-time integration, we show that a parabolic integration (corresponding to constant sensor acceleration) leads to motion blur that is invariant to object velocity. Thus, a single deconvolution kernel can be used to remove blur and create sharp images of scenes with objects moving at different speeds, without requiring any segmentation and without knowledge of the object speeds. Apart from motion invariance, we prove that the derived parabolic motion preserves image frequency content nearly optimally. That is, while static objects are degraded relative to their image from a static camera, a reliable reconstruction of all moving objects within a given velocities range is made possible. We have built a prototype camera and present successful deblurring results over a wide variety of human motions.

Mole pattern changes are important cues in detecting melanoma at an early stage. As a first step to automatically register mole pattern changes from skin images, this paper presents a framework to detect and label moles on skin images in the presence of clutter, occlusions, and varying imaging conditions. The input image is processed with cascaded blocks to successively discard non-mole pixels. Our method first searches the entire input image for skin regions using a non-parametric skin detection scheme, and the detected skin regions are further processed using a difference of Gaussian (DoG) filter to find possible mole candidates of varying sizes. Mole candidates are classified as moles in the final stage using a trained support vector machine. To increase the mole classification accuracy, hair is removed if present on the skin image using steerable filters and a graphical model. The performance of the designed system is evaluated with 28 test images, and the experimental results demonstrate the effectiveness of the proposed mole localization scheme.