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Low-Rankness Guided Group Sparse Representation for Image Restoration
Zha, Zhiyuan1; Wen, Bihan2; Yuan, Xin3; Zhou, Jiantao4; Zhu, Ce5; Kot, Alex Chichung1
2022-02
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Abstract

As a spotlighted nonlocal image representation model, group sparse representation (GSR) has demonstrated a great potential in diverse image restoration tasks. Most of the existing GSR-based image restoration approaches exploit the nonlocal self-similarity (NSS) prior by clustering similar patches into groups and imposing sparsity to each group coefficient, which can effectively preserve image texture information. However, these methods have imposed only plain sparsity over each individual patch of the group, while neglecting other beneficial image properties, e.g., low-rankness (LR), leads to degraded image restoration results. In this article, we propose a novel low-rankness guided group sparse representation (LGSR) model for highly effective image restoration applications. The proposed LGSR jointly utilizes the sparsity and LR priors of each group of similar patches under a unified framework. The two priors serve as the complementary priors in LGSR for effectively preserving the texture and structure information of natural images. Moreover, we apply an alternating minimization algorithm with an adaptively adjusted parameter scheme to solve the proposed LGSR-based image restoration problem. Extensive experiments are conducted to demonstrate that the proposed LGSR achieves superior results compared with many popular or state-of-the-art algorithms in various image restoration tasks, including denoising, inpainting, and compressive sensing (CS).

KeywordAdaptation Models Adaptively Adjusted Parameter Alternating Minimization Dictionaries Image Denoising Image Reconstruction Image Restoration Image Restoration Low-rankness Guided Group Sparse Representation (Lgsr) Minimization Nonlocal Self-similarity (Nss). Task Analysis
DOI10.1109/TNNLS.2022.3144630
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000754278700001
Scopus ID2-s2.0-85124740600
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Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorWen, Bihan
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.
2.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 (e-mail: bihan.wen@ntu.edu.sg)
3.School of Engineering, Westlake University, Hangzhou, Zhejiang 310024, China.
4.State Key Laboratory of Internet of Things for Smart City, and the Department of Computer and Information Science, University of Macau, Taipa, Macau 999078, China.
5.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-Rankness Guided Group Sparse Representation for Image Restoration[J]. IEEE Transactions on Neural Networks and Learning Systems,2022.
APA Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,Zhou, Jiantao,Zhu, Ce,&Kot, Alex Chichung.(2022).Low-Rankness Guided Group Sparse Representation for Image Restoration.IEEE Transactions on Neural Networks and Learning Systems.
MLA Zha, Zhiyuan,et al."Low-Rankness Guided Group Sparse Representation for Image Restoration".IEEE Transactions on Neural Networks and Learning Systems (2022).
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