NDLI: Image recovery and recognition: a combining method of matrix norm regularisation

Content Provider	IET Digital Library
Author	Wang, Wei Gang Song, Wei Wang, Guang Yuan Zeng, Guigen Tian, Feng
Abstract	The technology of image recovery, as a part of image processing, becomes more and more important. The robust principle component analysis (RPCA) serves as a key problem for low-rank matrix recovery. However, the existing methods for solving the RPCA are mostly based on nuclear norm minimisation. These methods have the disadvantage that matrix's rank cannot be well approximated because they minimise all singular values. Moreover, these methods will lead to instability when images are highly correlated. In this study, the authors set up a novel model which combines the truncated nuclear norm with Frobenius norm to enhance solution accuracy and stability. Based on the idea of elastic-net, which is composed of l 1 norm and l 2 norm, this model is based on elastic-net of the singular values can be composed of the truncated nuclear norm and the square of Frobenius norm. In order to solve this problem, the TNNF algorithm was proposed based on the alternating direction method of multipliers. Compared with traditional methods, the numerical simulation results show that the proposed TNNF can achieve higher accuracy and better stability.
Starting Page	1246
Ending Page	1253
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 8, Jun (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5511
Journal	IET Image Processing
Publisher Date	2019-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Alternating Direction Method of Multipliers Approximation Theory Computer Vision And Image Processing Technique Convex Programming Elastic-net Frobenius Norm Image Processing Image Recognition Image Recovery Interpolation And Function Approximation Linear Algebra Low-rank Matrix Recovery Matrix Norm Regularisation Minimisation Norm Nuclear Norm Minimisation Numerical Analysis Optimisation Technique Principal Component Analysis Robust Principle Component Analysis RPCA Singular Value Singular Value Decomposition Statistics TNNF Algorithm Truncated Nuclear Norm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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