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Nonlocal image denoising via adaptive tensor nuclear norm minimization
Zhang, Chenyang1; Hu, Wenrui2; Jin, Tianyu1; Mei, Zhonglei1; Chenyang Zhang
Source PublicationNEURAL COMPUTING & APPLICATIONS
2018
Volume29Issue:1Pages:3-19
SubtypeArticle
AbstractNonlocal self-similarity shows great potential in image denoising. Therefore, the denoising performance can be attained by accurately exploiting the nonlocal prior. In this paper, we model nonlocal similar patches through the multi-linear approach and then propose two tensor-based methods for image denoising. Our methods are based on the study of low-rank tensor estimation (LRTE). By exploiting low-rank prior in the tensor presentation of similar patches, we devise two new adaptive tensor nuclear norms (i.e., ATNN-1 and ATNN-2) for the LRTE problem. Among them, ATNN-1 relaxes the general tensor N-rank in a weighting scheme, while ATNN-2 is defined based on a novel tensor singular-value decomposition (t-SVD). Both ATNN-1 and ATNN-2 construct the stronger spatial relationship between patches than the matrix nuclear norm. Regularized by ATNN-1 and ATNN-2 respectively, the derived two LRTE algorithms are implemented through the adaptive singular-value thresholding with global optimal guarantee. Then, we embed the two algorithms into a residual-based iterative framework to perform nonlocal image denoising. Experiments validate the rationality of our tensor low-rank assumption, and the denoising results demonstrate that our proposed two methods are exceeding the state-of-the-art methods, both visually and quantitatively.
KeywordNonlocal Self-similarity Low-rank Tensor Estimation Singular-value Thresholding Tensor Nuclear Norm
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s00521-015-2050-5
WOS KeywordMATRIX COMPLETION ; ITERATIVE REGULARIZATION ; DECOMPOSITION ; ALGORITHM ; OPTIMIZATION ; RESTORATION ; FRAMEWORK ; SHRINKAGE
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000422933800002
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12260
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorChenyang Zhang
Affiliation1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Chenyang,Hu, Wenrui,Jin, Tianyu,et al. Nonlocal image denoising via adaptive tensor nuclear norm minimization[J]. NEURAL COMPUTING & APPLICATIONS,2018,29(1):3-19.
APA Zhang, Chenyang,Hu, Wenrui,Jin, Tianyu,Mei, Zhonglei,&Chenyang Zhang.(2018).Nonlocal image denoising via adaptive tensor nuclear norm minimization.NEURAL COMPUTING & APPLICATIONS,29(1),3-19.
MLA Zhang, Chenyang,et al."Nonlocal image denoising via adaptive tensor nuclear norm minimization".NEURAL COMPUTING & APPLICATIONS 29.1(2018):3-19.
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