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LOW-RANK REGULARIZED JOINT SPARSITY FOR IMAGE DENOISING
Zha, Zhiyuan1; Wen, Bihan1; Yuan, Xin2; Zhou, Jiantao3; Zhu, Ce4
2021
Conference Name2021 IEEE International Conference on Image Processing (ICIP)
Source PublicationProceedings - International Conference on Image Processing, ICIP
Volume2021-September
Pages1644-1648
Conference Date19-22 September 2021
Conference PlaceAnchorage, AK, USA
Abstract

Nonlocal sparse representation models such as group sparse representation (GSR), low-rankness and joint sparsity (JS) have shown great potentials in image denoising studies, by effectively exploiting image nonlocal self-similarity (NSS) property. Popular dictionary-based JS algorithms apply convex JS penalties in their objective functions, which avoid NP-hard sparse coding step, but lead to only approximately sparse representation. Such approximated JS models fail to impose low-rankness of the underlying image data, resulting in degraded quality in image restoration. To simultaneously exploit the low-rank and JS priors, we propose a novel low-rank regularized joint sparsity model, dubbed LRJS, to enhance the dependency (i.e., low-rankness) of similar patches, thus better suppress independent noise. Moreover, to make the optimization tractable and robust, an alternating minimization algorithm with an adaptive parameter adjustment strategy is developed to solve the proposed LRJS-based image denoising problem. Experimental results demonstrate that the proposed LRJS outperforms many popular or state-of-the-art denoising algorithms in terms of both objective and visual perception metrics.

KeywordAdaptive Parameter Alternating Minimization Image Denoising Low-rank Regularized Joint Sparsity Nonlocal Sparse Representation
DOI10.1109/ICIP42928.2021.9506726
URLView the original
Language英語English
Scopus ID2-s2.0-85125567680
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Document TypeConference paper
CollectionFaculty of Science and Technology
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore
2.Nokia Bell Labs, Murray Hill, 600 Mountain Avenue, 07974, United States
3.Department of Computer and Information Science, University of Macau, 999078, Macao
4.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
Recommended Citation
GB/T 7714
Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,et al. LOW-RANK REGULARIZED JOINT SPARSITY FOR IMAGE DENOISING[C],2021:1644-1648.
APA Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,Zhou, Jiantao,&Zhu, Ce.(2021).LOW-RANK REGULARIZED JOINT SPARSITY FOR IMAGE DENOISING.Proceedings - International Conference on Image Processing, ICIP,2021-September,1644-1648.
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