Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

커널에 의한 비근접 부분영상과 저차수 영상을 이용한 영상 선명화 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 유석인.Blind image deblurring aims to restore a high-quality image from a blurry image. Blind image deblurring has gained considerable attention in recent years because it involves many challenges in problem formulation, regularization, and optimization. In optimization perspective, blind image deblurring is a severely ill-posed inverse problemtherefore, effective regularizations are required in order to obtain a high-quality latent image from a single blurred one. In this paper, we propose nonlocal regularizations to improve blind image deblurring. First, we propose to use the nonlocal patches selected by similarity weighted by the kernel for the next blur-kernel estimation. Using these kernel-guided nonlocal patches, we impose regularization that nonlocal patches would produce the similar values by convolution. Imposing this regularization improves the kernel estimation. Second, we propose to use a nonlocal low-rank image obtained from the composition of nonlocal similar patches. Using this nonlocal low-rank image, we impose regularization that the latent image is similar to this nonlocal low-rank image. A nonlocal low-rank image contains less noise by its intrinsic property. Imposing this regularization improves the estimation of the latent image with less noise. We evaluated our method quantitatively and qualitatively by comparing several conventional blind deblurring methods. For the quantitative evaluation, we computed the sum of squared error, peak signal-to-noise ratio, and structural similarity index. For blurry images without noise, our method was generally superior to the other methods. Especially, the results of ours were sharper on structures and smoother on flat regions. For blurry and noisy images, our method highly outperformed the conventional methods. Most of other methods could not successfully estimate the blur-kernel, and the image blur was not removed. On the other hand, our method successfully estimate the blur-kernel by overcoming the noise and restored a high-quality of deblurred image with less noise.Chapter 1 Introduction 1 1.1 Formulation of the Blind Image Deblurring 2 1.2 Approach 4 1.2.1 The Use of Kernel-guided Nonlocal Patches 4 1.2.2 The Use of Nonlocal Low-rank Images 5 1.3 Overview 5 Chapter 2 Related Works 6 2.1 Natural Image Prior 7 2.1.1 Scale Mixture of Gaussians 8 2.1.2 Hyper-Laplacian Distribution 8 2.2 Avoiding No-blur Solution 10 2.2.1 Marginalization over Possible Images 11 2.2.2 Normalization of l1 by l2 13 2.2.3 Alternating I and k Approach 15 2.3 Sparse Representation 17 2.4 Using Sharp Edges 19 2.5 Handling Noise 20 Chapter 3 Preliminary: Optimization 24 3.1 Iterative Reweighted Least Squares (IRLS) 25 3.1.1 Least Squared Error Approximation 26 3.1.2 Weighted Least Squared Error Approximation 26 3.1.3 The lp Norm Approximation of Overdetermined System 27 3.1.4 The lp Norm Approximation of Underdetermined System 28 3.2 Optimization using Conjugacy 29 3.2.1 The Conjugate Direction Method 30 3.2.2 The Conjugate Gradient Method 33 3.3 The Singular Value Thresholding Algorithm 36 Chapter 4 Extracting Salient Structures 39 4.1 Structure-Texture Decomposition with Uniform Edge Map 39 4.2 Structure-Texture Decomposition with Adaptive Edge Map 41 4.3 Enhancing Structures and Producing Salient Edges 43 4.4 Analysis on the Method of Extracting Salient Edges 44 Chapter 5 Blind Image Deblurring using Nonlocal Patches 46 5.1 Estimating a Blur-kernel using Kernel-guided Nonlocal Patches 47 5.1.1 Sparse Prior 48 5.1.2 Continuous Prior 48 5.1.3 Nonlocal Prior by Kernel-guided Nonlocal Patches 49 5.2 Estimating an Interim Image using Nonlocal Low-rank Images 52 5.2.1 Nonlocal Low-rank Prior 52 5.3 Multiscale Implementation 55 5.4 Latent Image Estimation 56 Chapter 6 Experimental Results 58 6.1 Images with Ground Truth 61 6.2 Images without Ground Truth 105 6.3 Analysis on Preprocessing using Denoising 111 6.4 Analysis on the Size of Nonlocal Patches 121 6.5 Time Performance 125 Chapter 7 Conclusion 126 Bibliography 129 요약 140Docto

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented

Data-Driven Image Restoration

Every day many images are taken by digital cameras, and people are demanding visually accurate and pleasing result. Noise and blur degrade images captured by modern cameras, and high-level vision tasks (such as segmentation, recognition, and tracking) require high-quality images. Therefore, image restoration specifically, image deblurring and image denoising is a critical preprocessing step. A fundamental problem in image deblurring is to recover reliably distinct spatial frequencies that have been suppressed by the blur kernel. Existing image deblurring techniques often rely on generic image priors that only help recover part of the frequency spectrum, such as the frequencies near the high-end. To this end, we pose the following specific questions: (i) Does class-specific information offer an advantage over existing generic priors for image quality restoration? (ii) If a class-specific prior exists, how should it be encoded into a deblurring framework to recover attenuated image frequencies? Throughout this work, we devise a class-specific prior based on the band-pass filter responses and incorporate it into a deblurring strategy. Specifically, we show that the subspace of band-pass filtered images and their intensity distributions serve as useful priors for recovering image frequencies. Next, we present a novel image denoising algorithm that uses external, category specific image database. In contrast to existing noisy image restoration algorithms, our method selects clean image “support patches” similar to the noisy patch from an external database. We employ a content adaptive distribution model for each patch where we derive the parameters of the distribution from the support patches. Our objective function composed of a Gaussian fidelity term that imposes category specific information, and a low-rank term that encourages the similarity between the noisy and the support patches in a robust manner. Finally, we propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising. The CIMM structure possesses two distinctive features that are important for the noise removal task. Firstly, each residual unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions. Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer

Second-Order Regression-Based MR Image Upsampling

The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation