CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction
Xie, Yuan1,2; Gu, Shuhang3; Liu, Yan3; Zuo, Wangmeng4; Zhang, Wensheng2; Zhang, Lei3; Yuan Xie
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2016-10-01
Volume25Issue:10Pages:4842-4857
SubtypeArticle
AbstractLow rank matrix approximation (LRMA), which aims to recover the underlying low rank matrix from its degraded observation, has a wide range of applications in computer vision. The latest LRMA methods resort to using the nuclear norm minimization (NNM) as a convex relaxation of the nonconvex rank minimization. However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications. We propose a more flexible model, namely, the weighted Schatten p-norm minimization (WSNM), to generalize the NNM to the Schatten p-norm minimization with weights assigned to different singular values. The proposed WSNM not only gives better approximation to the original low-rank assumption, but also considers the importance of different rank components. We analyze the solution of WSNM and prove that, under certain weights permutation, WSNM can be equivalently transformed into independent non-convex l(p)-norm subproblems, whose global optimum can be efficiently solved by generalized iterated shrinkage algorithm. We apply WSNM to typical low-level vision problems, e.g., image denoising and background subtraction. Extensive experimental results show, both qualitatively and quantitatively, that the proposed WSNM can more effectively remove noise, and model the complex and dynamic scenes compared with state-of-the-art methods.
KeywordLow Rank Weighted Schatten P-norm Low-level Vision
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2016.2599290
WOS KeywordRANK MINIMIZATION ; MATRIX COMPLETION ; MISSING DATA ; APPROXIMATION ; FACTORIZATION ; RESTORATION ; ALGORITHMS ; SIGNALS
Indexed BySCI
Language英语
Funding OrganizationHong Kong Scholars Program ; HK RGC GRF(PolyU 5313/13E) ; National Natural Science Foundation of China(61402480 ; 61432008 ; 61472423 ; 61502495 ; 41401383 ; 61373077)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000382677700008
Citation statistics
Cited Times:43[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12447
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorYuan Xie
Affiliation1.Hong Kong Polytech Univ, Dept Comp, Visual Comp Lab, Hong Kong, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
3.Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
4.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Peoples R China
Recommended Citation
GB/T 7714
Xie, Yuan,Gu, Shuhang,Liu, Yan,et al. Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(10):4842-4857.
APA Xie, Yuan.,Gu, Shuhang.,Liu, Yan.,Zuo, Wangmeng.,Zhang, Wensheng.,...&Yuan Xie.(2016).Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(10),4842-4857.
MLA Xie, Yuan,et al."Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.10(2016):4842-4857.
Files in This Item: Download All
File Name/Size DocType Version Access License
my_wsnm.pdf(7732KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xie, Yuan]'s Articles
[Gu, Shuhang]'s Articles
[Liu, Yan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xie, Yuan]'s Articles
[Gu, Shuhang]'s Articles
[Liu, Yan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xie, Yuan]'s Articles
[Gu, Shuhang]'s Articles
[Liu, Yan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: my_wsnm.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.