Blind motion deblurring using multiple images

Cai, Jian-Feng; Ji, Hui; Liu, Chaoqiang; Shen, Zuowei

doi:10.1016/j.jcp.2009.04.022

Cited by 100 publications

(60 citation statements)

References 29 publications

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“…This paper reduces the requirement to α > α ∞ for a certain finite α ∞ . Before ending this subsection, we list some applications of the linearized Bregman method that have appeared in the literature: compressed sensing [67,52,13], the matrix completion problem [10], and image deblurring [15,12,11]. Good numerical performance is reported in these papers.…”

Section: The Linearized Bregman Method the Linearized Bregman Methodsmentioning

confidence: 99%

Analysis and Generalizations of the Linearized Bregman Method

Yin¹

2010

SIAM J. Imaging Sci.

185

189

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Abstract. This paper analyzes and improves the linearized Bregman method for solving the basis pursuit and related sparse optimization problems. The analysis shows that the linearized Bregman method has the exact regularization property; namely, it converges to an exact solution of the basis pursuit problem whenever its smooth parameter α is greater than a certain value. The analysis is based on showing that the linearized Bregman algorithm is equivalent to gradient descent applied to a certain dual formulation. This result motivates generalizations of the algorithm enabling the use of gradient-based optimization techniques such as line search, Barzilai-Borwein, limited memory BFGS (L-BFGS), nonlinear conjugate gradient, and Nesterov's methods. In the numerical simulations, the two proposed implementations, one using Barzilai-Borwein steps with nonmonotone line search and the other using L-BFGS, gave more accurate solutions in much shorter times than the basic implementation of the linearized Bregman method with a so-called kicking technique.

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Section: The Linearized Bregman Method the Linearized Bregman Methodsmentioning

confidence: 99%

Analysis and Generalizations of the Linearized Bregman Method

Yin¹

2010

SIAM J. Imaging Sci.

185

189

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“…This could be because this background region had only textures of rather random patterns but no strong edges; therefore, there were no distinct image features available for kernel estimation. Note that BD in such regions is in principle highly ill-posed, and to address this issue, we might need to resort to more radical approaches such as multi-observation [37]. It is noteworthy that the proposed method achieved these results without image-dependent tuning of parameters, e.g., numbers of segments, nor prior knowledge on blur types.…”

Section: Methodsmentioning

confidence: 95%

Shift-Variant Blind Deconvolution Using a Field of Kernels

Sonogashira

Iiyama

Minoh

2017

IEICE Trans. Inf. & Syst.

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Motoharu SONOGASHIRA†a) , Nonmember, Masaaki IIYAMA †b) , Member, and Michihiko MINOH †c) , Fellow SUMMARY Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variationalBayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches. key words: blind deconvolution, deblurring, shift-variant, variational Bayes IntroductionBlind deconvolution (BD) for deblurring is one of the most extensively studied topics in image processing [1], [2]. Essentially, blurring of images is modeled as convolution of images and kernels. Deconvolution is the inverse of this process, which effectively restores sharp images from blurry ones. The objective of BD is to perform deconvolution when kernels are unknown, which is often the case in practice [1]. Since undesirable blur is quite common in the real world, BD has a wide range of applications, e.g., mobile photography [3], computational photography [4], computer vision [5], astronomical imaging [6], and biomedical imaging [7].Traditional shift-invariant (SI) BD assumes uniform blur produced by kernels that do not vary spatially across images, such as the example in Fig. 1 (b). However, in reality blur kernels often spatially vary, producing nonuni- . For example, independently moving objects produce different effects of motion blur, and objects distant from a camera are more subject to defocus blur than close objects. Blur is often more complex since both motion and defocus blur can simultaneously occur, as shown in Fig. 1 (c). In such cases, SI BD methods fail in kernel estimation and also in deconvolution [1]. Although specialized BD methods for nonuniform blur have been recently developed, they can only handle certain types of blur, e.g., motion, defocus, or locally uniform blur [2]. Consequently, the applicability of BD to general blurry images remains limited.I...

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“…In this method [4][5][6][7], sharp image and blurring kernels are estimated using two or more images. Hence two images are taken using different exposure timings.…”

Section: Multi Image Deblurringmentioning

confidence: 99%

“…We would like to thank the authors of [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] for kindly providing their essential information and codes and would also like to thank my professors , teaching and non-teaching staff of my department for their valuable comments and suggestions which helps us to complete this work successfully.…”

Section: Acknowledgementsmentioning

confidence: 99%

A New Weighted Average Filter for Removing Camera Shake

Prasad¹,

Srinivas²,

Kumar³

2016

IJCA

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Image blurring is one of the major problems in the field of digital image processing. Generally, camera shake causes blurring. As a result, uneven blur kernel is present in the image which is random in nature. Therefore, every image in the burst is blurred in a different way. Deblurred image can be obtained using single image or multiple images. A clean sharp image is recovered by fusing the group of images without calculating the blurring kernel. In this paper, a new technique called a new weighted average filter is introduced for removing camera shake using single or multiple images. This technique takes a burst of images and calculates a weighted average in the Discrete Wavelet domain, where the weights of images depend on their Discrete Wavelet Spectrum magnitudes.

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Blind motion deblurring using multiple images

Cited by 100 publications

References 29 publications

Analysis and Generalizations of the Linearized Bregman Method

Analysis and Generalizations of the Linearized Bregman Method

Shift-Variant Blind Deconvolution Using a Field of Kernels

A New Weighted Average Filter for Removing Camera Shake

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