Compressive Sensing via Nonlocal Low-Rank Regularization

doi:10.1109/TIP.2014.2329449

	Compressive Sensing via Nonlocal Low-Rank Regularization
	Dong, Weisheng 1; Shi, Guangming 1; Li, Xin 2; Ma, Yi3 ; Huang, Feng 4
	2014-08
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING (IF:10.8[JCR-2023],12.1[5-Year])
ISSN	1057-7149
卷号	23 期号:8 页码:3618-3632
发表状态	已发表
DOI	10.1109/TIP.2014.2329449
摘要	Sparsity has been widely exploited for exact reconstruction of a signal from a small number of random measurements. Recent advances have suggested that structured or group sparsity often leads to more powerful signal reconstruction techniques in various compressed sensing (CS) studies. In this paper, we propose a nonlocal low-rank regularization (NLR) approach toward exploiting structured sparsity and explore its application into CS of both photographic and MRI images. We also propose the use of a nonconvex log det(X) as a smooth surrogate function for the rank instead of the convex nuclear norm and justify the benefit of such a strategy using extensive experiments. To further improve the computational efficiency of the proposed algorithm, we have developed a fast implementation using the alternative direction multiplier method technique. Experimental results have shown that the proposed NLR-CS algorithm can significantly outperform existing state-of-the-art CS techniques for image recovery.
关键词	Compresses sensing low-rank approximation structured sparsity nonconvex optimization alternative direction multiplier method
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	NSF[ECCS-0968730]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000340094000002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20143017971565
EI主题词	Compressed sensing ; Magnetic resonance imaging
EI分类号	Information Theory and Signal Processing:716.1 ; Imaging Techniques:746
WOS关键词	IMAGE-RECONSTRUCTION ; THRESHOLDING ALGORITHM ; SPARSITY ; SIGNAL ; RECOVERY
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/2384
专题	信息科学与技术学院信息科学与技术学院_PI研究组_马毅组
通讯作者	Dong, Weisheng
作者单位	1.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China 2.W Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA 3.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai 200444, Peoples R China 4.Philips Healthcare, Suzhou 234000, Peoples R China
推荐引用方式 GB/T 7714	Dong, Weisheng,Shi, Guangming,Li, Xin,et al. Compressive Sensing via Nonlocal Low-Rank Regularization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(8):3618-3632.
APA	Dong, Weisheng,Shi, Guangming,Li, Xin,Ma, Yi,&Huang, Feng.(2014).Compressive Sensing via Nonlocal Low-Rank Regularization.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(8),3618-3632.
MLA	Dong, Weisheng,et al."Compressive Sensing via Nonlocal Low-Rank Regularization".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.8(2014):3618-3632.