UM  > Faculty of Science and Technology
Affiliated with RCfalse
Status已發表Published
A Hybrid Structural Sparsification Error Model for Image Restoration
Zha, Zhiyuan1; Wen, Bihan2; Yuan, Xin3; Zhou, Jiantao4; Zhu, Ce5; Kot, Alex Chichung1
2022-09
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
ISSN2162-237X
Volume33Issue:9Pages:4451-4465
Abstract

Recent works on structural sparse representation (SSR), which exploit image nonlocal self-similarity (NSS) prior by grouping similar patches for processing, have demonstrated promising performance in various image restoration applications. However, conventional SSR-based image restoration methods directly fit the dictionaries or transforms to the internal (corrupted) image data. The trained internal models inevitably suffer from overfitting to data corruption, thus generating the degraded restoration results. In this article, we propose a novel hybrid structural sparsification error (HSSE) model for image restoration, which jointly exploits image NSS prior using both the internal and external image data that provide complementary information. Furthermore, we propose a general image restoration scheme based on the HSSE model, and an alternating minimization algorithm for a range of image restoration applications, including image inpainting, image compressive sensing and image deblocking. Extensive experiments are conducted to demonstrate that the proposed HSSE-based scheme outperforms many popular or state-of-the-art image restoration methods in terms of both objective metrics and visual perception.

KeywordAnalytical Models Data Models Dictionaries Hybrid Structural Sparsification Error (Hsse) Image Denoising Image Restoration Image Restoration Minimization Nonlocal Self-similarity (Nss) Structural Sparse Representation (Ssr). Transforms
DOI10.1109/TNNLS.2021.3057439
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:000733450000001
Scopus ID2-s2.0-85101759362
Fulltext Access
Citation statistics
Cited Times [WOS]:14   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
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.Nokia Bell Labs, Murray Hill, Berkeley Heights, NJ 07974 USA.
4.State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau, 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. A Hybrid Structural Sparsification Error Model for Image Restoration[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,33(9):4451-4465.
APA Zha, Zhiyuan,Wen, Bihan,Yuan, Xin,Zhou, Jiantao,Zhu, Ce,&Kot, Alex Chichung.(2022).A Hybrid Structural Sparsification Error Model for Image Restoration.IEEE Transactions on Neural Networks and Learning Systems,33(9),4451-4465.
MLA Zha, Zhiyuan,et al."A Hybrid Structural Sparsification Error Model for Image Restoration".IEEE Transactions on Neural Networks and Learning Systems 33.9(2022):4451-4465.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zha, Zhiyuan]'s Articles
[Wen, Bihan]'s Articles
[Yuan, Xin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.