UM  > Faculty of Science and Technology
Affiliated with RCfalse
Image Restoration via Simultaneous Nonlocal Self-Similarity Priors
Zhiyuan Zha1; Xin Yuan2; Jiantao Zhou3,4; Ce Zhu5; Bihan Wen1
Source PublicationIEEE Transactions on Image Processing

Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar patches to construct patch groups, recent studies have revealed that structural sparse representation (SSR) models can achieve promising performance in various image restoration tasks. However, most existing SSR methods only exploit the NSS prior from the input degraded (internal) image, and few methods utilize the NSS prior from external clean image corpus; how to jointly exploit the NSS priors of internal image and external clean image corpus is still an open problem. In this article, we propose a novel approach for image restoration by simultaneously considering internal and external nonlocal self-similarity (SNSS) priors that offer mutually complementary information. Specifically, we first group nonlocal similar patches from images of a training corpus. Then a group-based Gaussian mixture model (GMM) learning algorithm is applied to learn an external NSS prior. We exploit the SSR model by integrating the NSS priors of both internal and external image data. An alternating minimization with an adaptive parameter adjusting strategy is developed to solve the proposed SNSS-based image restoration problems, which makes the entire algorithm more stable and practical. Experimental results on three image restoration applications, namely image denoising, deblocking and deblurring, demonstrate that the proposed SNSS produces superior results compared to many popular or state-of-the-art methods in both objective and perceptual quality measurements.

KeywordImage Restoration Structural Sparse Representation Simultaneous Nonlocal Self-similarity Gaussian Mixture Model Adaptive Parameter Adjusting
URLView the original
Indexed BySCIE
WOS Research AreaEngineering ; Computer Science
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000564245800003
Scopus ID2-s2.0-85090841169
Fulltext Access
Citation statistics
Cited Times [WOS]:39   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorBihan Wen
Affiliation1.School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798
2.Nokia Bell Labs, Murray Hill, NJ 07974 USA
3.State Key Laboratory of Internet of Things for Smart City, University of Macau, Taipa, Macau
4.e Department of Computer and Information Science, University of Macau, Taipa, Macau
5.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Recommended Citation
GB/T 7714
Zhiyuan Zha,Xin Yuan,Jiantao Zhou,et al. Image Restoration via Simultaneous Nonlocal Self-Similarity Priors[J]. IEEE Transactions on Image Processing,2020,29:8561-8576.
APA Zhiyuan Zha,Xin Yuan,Jiantao Zhou,Ce Zhu,&Bihan Wen.(2020).Image Restoration via Simultaneous Nonlocal Self-Similarity Priors.IEEE Transactions on Image Processing,29,8561-8576.
MLA Zhiyuan Zha,et al."Image Restoration via Simultaneous Nonlocal Self-Similarity Priors".IEEE Transactions on Image Processing 29(2020):8561-8576.
Files in This Item: Download All
File Name/Size Publications Version Access License
Image_Restoration_vi(7462KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhiyuan Zha]'s Articles
[Xin Yuan]'s Articles
[Jiantao Zhou]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhiyuan Zha]'s Articles
[Xin Yuan]'s Articles
[Jiantao Zhou]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhiyuan Zha]'s Articles
[Xin Yuan]'s Articles
[Jiantao Zhou]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Image_Restoration_via_Simultaneous_Nonlocal_Self-Similarity_Priors.pdf
Format: Adobe PDF
All comments (0)
No comment.

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