文献类型：期刊文献

英文题名：Convex optimization based low-rank matrix decomposition for image restoration

作者：He, Ning[1];Wang, Jin-Bao[1];Zhang, Lu-Lu[1];Lu, Ke[2]

第一作者：何宁

通讯作者：He, N[1]

机构：[1]Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室|北京联合大学智慧城市学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Informat Technol, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China.|[1141734]北京联合大学智慧城市学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2016

卷号：172

起止页码：253-261

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20152200891368);Scopus(收录号：2-s2.0-84946501273);WOS:【SCI-EXPANDED(收录号:WOS:000364884700026)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant nos. 61370138, 61271435, 61202245, 61103130, U1301251); National Program on Key Basic Research Projects (973 programs) (Grant nos. 2010CB731804-1, 2011CB706901-4); The Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (Nos. IDHT20130513, CIT&TCD20130513), Beijing Municipal Natural Science Fundation (Nos. 4141003, 4152017).

语种：英文

外文关键词：Image restoration; Low-rank matrix; Principal component pursuit; Singular value thresholding

摘要：This paper addresses the problem of image denoising in the presence of significant corruption. Our method seeks an optimal set of image domain transformations such that the matrix of transformed images can be decomposed as the sum of a sparse matrix of errors and a low-rank matrix of recovered denoised images. We reduce this optimization problem to a sequence of convex programs minimizing the sum of the l(1) - norm and the nuclear norm of the two component matrices, which can be solved efficiently using scalable convex optimization techniques. We verify the efficacy of the proposed image denoising algorithm through extensive experiments on both numerical simulations and different types of images, demonstrating its highly competent objective performance compared with several state-of-the-art methods for matrix decomposition and image denoising. Our subjective quality results compare favorably with those obtained by existing techniques, especially at high noise levels and with a large amount of missing data. (C) 2015 Elsevier B.V. All rights reserved.