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Nonlocal image denoising via adaptive tensor nuclear norm minimization

文献类型：期刊论文

作者Zhang, Chenyang1; Hu, Wenrui2; Jin, Tianyu1; Mei, Zhonglei1; Chenyang Zhang
刊名NEURAL COMPUTING & APPLICATIONS
出版日期2018
卷号29期号:1页码:3-19
关键词Nonlocal Self-similarity Low-rank Tensor Estimation Singular-value Thresholding Tensor Nuclear Norm
DOI10.1007/s00521-015-2050-5
文献子类Article
英文摘要Nonlocal 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.
WOS关键词MATRIX COMPLETION ; ITERATIVE REGULARIZATION ; DECOMPOSITION ; ALGORITHM ; OPTIMIZATION ; RESTORATION ; FRAMEWORK ; SHRINKAGE
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000422933800002
源URL[http://ir.ia.ac.cn/handle/173211/12260]  
专题精密感知与控制研究中心_精密感知与控制
通讯作者Chenyang Zhang
作者单位1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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