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

Motion-aware noise filtering for deblurring of noisy and blurry images

Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples. 1

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

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