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Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models
Zha, Zhiyuan1; Wen, Bihan1; Yuan, Xin2; Zhou, Jiantao3; Zhu, Ce4
2021
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Volume30Pages:5223-5238
AbstractImage nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g., JS enforces the sparse codes to share the same support, or too general, e.g., GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC), to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.
Keywordadaptive parameter adjustment alternating minimization Group sparse coding image restoration low-rank regularized group sparse coding
DOI10.1109/TIP.2021.3078329
URLView the original
Language英語English
Scopus ID2-s2.0-85107000291
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Cited Times [WOS]:20   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
2.Nokia Bell Labs, Murray Hill, United States
3.Department of Computer and Information Science, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, Macao
4.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
Recommended Citation
GB/T 7714
Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,et al. Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models[J]. IEEE Transactions on Image Processing,2021,30:5223-5238.
APA Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,Zhou, Jiantao,&Zhu, Ce.(2021).Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models.IEEE Transactions on Image Processing,30,5223-5238.
MLA Zha, Zhiyuan,et al."Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models".IEEE Transactions on Image Processing 30(2021):5223-5238.
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