NDLI: A Douglas–Rachford Splitting Approach to Compressed Sensing Image Recovery Using Low-Rank Regularization

Content Provider	IEEE Xplore Digital Library
Author	Shuangjiang Li Hairong Qi
Copyright Year	1992
Abstract	In this paper, we study the compressed sensing (CS) image recovery problem. The traditional method divides the image into blocks and treats each block as an independent sub-CS recovery task. This often results in losing global structure of an image. In order to improve the CS recovery result, we propose a nonlocal (NL) estimation step after the initial CS recovery for denoising purpose. The NL estimation is based on the well-known NL means filtering that takes an advantage of self-similarity in images. We formulate the NL estimation as the low-rank matrix approximation problem, where the low-rank matrix is formed by the NL similarity patches. An efficient algorithm, nonlocal Douglas-Rachford (NLDR), based on Douglas-Rachford splitting is developed to solve this low-rank optimization problem constrained by the CS measurements. Experimental results demonstrate that the proposed NLDR algorithm achieves significant performance improvements over the state-of-the-art in CS image recovery.
Sponsorship	IEEE Signal Processing Society
Starting Page	4240
Ending Page	4249
Page Count	10
File Size	6311251
File Format	PDF
ISSN	10577149
Volume Number	24
Issue Number	11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Image reconstruction Approximation methods Optimization Compressed sensing Noise reduction Approximation algorithms low-rank estimation image recovery nonlocal filtering Douglas-Rachford splitting
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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