BM3D Frames and Variational Image Deblurring

Danielyan, Aram; Katkovnik, Vladimir; Егиазарян, Карен

doi:10.1109/tip.2011.2176954

Cited by 566 publications

(429 citation statements)

References 24 publications

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“…The basic parameters of NCLR are as follows: the patch size is 6 × 6, total L = 40 similar patches are selected for each chosen exemplar and p = 0.8. We compare the proposed NCLR deblurring method with four recently image deblurring methods, including the constrained TV deblurring (denoted by FISTA) method [25], the IDD-BM3D deblurring method [26], the centralized sparse representation deblurring (CSR) method [20] and the nonlocally centralized sparse representation deblurring (NCSR) method [21].…”

Section: Image Deblurringmentioning

confidence: 99%

Non-Convex Low-Rank Approximation for Image Denoising and Deblurring

Yang

Song

2016

IEICE Trans. Inf. & Syst.

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SUMMARY Recovery of low-rank matrices has seen significant activity in many areas of science and engineering, motivated by theoretical results for exact reconstruction guarantees and interesting practical applications. Recently, numerous methods incorporated the nuclear norm to pursue the convexity of the optimization. However, this greatly restricts its capability and flexibility in dealing with many practical problems, where the singular values have clear physical meanings. This paper studies a generalized non-convex low-rank approximation, where the singular values are in l p −heuristic. Then specific results are derived for image restoration, including denoising and deblurring. Extensive experimental results on natural images demonstrate the improvement of the proposed method over the recent image restoration methods.

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Section: Image Deblurringmentioning

confidence: 99%

Non-Convex Low-Rank Approximation for Image Denoising and Deblurring

Yang

Song

2016

IEICE Trans. Inf. & Syst.

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“…This model proposed by Dabov et al, is divided into two similar steps. In this paper combining the definitions from Reference [9] and Reference [11], we describe it as three segments.…”

Section: Bm3d Framementioning

confidence: 99%

“…In 2009, YiFei Lou et al introduced NL method into RL algorithms [7]. In 2011, through the analysis and synthesis methods, Aram et al transferred fuzzy image restoration problems with noise into to the associated fuzzy and denoising problems [8,9], made BM3D and other denoising methods can be directly used in noise image fuzzy, and achieved obvious effects. But the current recovery methods are mostly based on the assumption of blurred images having moved the same nature, so it is difficult to effectively recover signals with noise for radial blur this shift becomes blurred image.…”

Section: Introductionmentioning

confidence: 99%

Noisy Radial-blurred Images Restoration Using the BM3D Frames

Chonglun¹,

Liu²,

Yang³

2014

TOCSJ

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BM3D frames based formulation provides new perspectives for the use of BM3D modeling in the circular shift-invariant reconstruction, but cannot be applied in the non-variant reconstruction. In order to solve this problem, a radial blur physical model was established, in this paper rectangular block replaced sector block in rectangular coordinate image, the matching area and restoration coefficient regulation were established to improve the BM3D frames and to complete the radial image restoration. The results show that the sector block was matching in neighbor area, the radial blur image can be effectively recovered, and the effect is obviously better than RL-NL and RL-TV methods or other restoration methods.

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“…Even more, a denoiser as a component of the algorithm appears as a solution in many variational setups (e.g. [9], [10]). …”

Section: Introductionmentioning

confidence: 99%

Complex domain nonlocal block-matching denoising based on high-order singular value decomposition (HOSVD)

Katkovnik

Ponomarenko

Егиазарян

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Block matching 3D collaborative filtering (BM3D) is one of the most popular denoising technique based on data sparsity concept applied to specially structured data. In this paper we develop this technique for complex domain, i.e. for application to complex-valued data. Sparsity as an approximation technique can be addressed directly to complex-valued variables or to real-valued pairs phase/amplitude and real/imaginary parts of complex-valued variables. As a result we arrive to various ways of development and obtain a set of quite different algorithms. The algorithms proposed in this paper are composed from two components: nonlocal patch-wise grouping and highorder singular value decomposition (HOSVD) for grouped data processing. The latter gives data adaptive complex-valued bases for complex-valued data or real-valued bases for joint processing of the pairs phase/amplitude, real/imaginary parts of complexvalued variables. Comparative study of the developed algorithms is produced in order to select the most efficient ones.

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BM3D Frames and Variational Image Deblurring

Cited by 566 publications

References 24 publications

Non-Convex Low-Rank Approximation for Image Denoising and Deblurring

Non-Convex Low-Rank Approximation for Image Denoising and Deblurring

Noisy Radial-blurred Images Restoration Using the BM3D Frames

Complex domain nonlocal block-matching denoising based on high-order singular value decomposition (HOSVD)

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