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Image Restoration Using Joint Patch-Group-Based Sparse Representation
Zhiyuan Zha1; Xin Yuan2; Bihan Wen3; Jiachao Zhang4; Jiantao Zhou5,6; Ce Zhu1
2020-07-03
Source PublicationIEEE Transactions on Image Processing
ISSN1057-7149
Volume29Pages:7735-7750
Abstract

Sparse representation has achieved great success in various image processing and computer vision tasks. For image processing, typical patch-based sparse representation (PSR) models usually tend to generate undesirable visual artifacts, while group-based sparse representation (GSR) models lean to produce over-smooth effects. In this paper, we propose a new sparse representation model, termed joint patch-group based sparse representation (JPG-SR). Compared with existing sparse representation models, the proposed JPG-SR provides an effective mechanism to integrate the local sparsity and nonlocal self-similarity of images. We then apply the proposed JPG-SR to image restoration tasks, including image inpainting and image deblocking. An iterative algorithm based on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed JPG-SR based image restoration problems. Experimental results demonstrate that the proposed JPG-SR is effective and outperforms many state-of-the-art methods in both objective and perceptual quality.

KeywordSparse Representation Jpg-sr Nonlocal Self-similarity Image Restoration Admm
DOI10.1109/TIP.2020.3005515
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000549387700004
Scopus ID2-s2.0-85088519299
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Cited Times [WOS]:8   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorCe Zhu
Affiliation1.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2.Nokia Bell Labs, Murray Hill, NJ 07974 USA
3.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798
4.Artificial Intelligence Institute of Industrial Technology, Nanjing Institute of Technology, Nanjing 211167, China
5.State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macau
6.Department of Computer and Information Science, University of Macau, Taipa, Macau
Recommended Citation
GB/T 7714
Zhiyuan Zha,Xin Yuan,Bihan Wen,et al. Image Restoration Using Joint Patch-Group-Based Sparse Representation[J]. IEEE Transactions on Image Processing,2020,29:7735-7750.
APA Zhiyuan Zha,Xin Yuan,Bihan Wen,Jiachao Zhang,Jiantao Zhou,&Ce Zhu.(2020).Image Restoration Using Joint Patch-Group-Based Sparse Representation.IEEE Transactions on Image Processing,29,7735-7750.
MLA Zhiyuan Zha,et al."Image Restoration Using Joint Patch-Group-Based Sparse Representation".IEEE Transactions on Image Processing 29(2020):7735-7750.
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