NDLI: Tri-decomposition model for image recovery

Content Provider	IET Digital Library
Author	Jin, Mengying Chen, Yunjie Zhang, Fanlong
Abstract	This Letter reveals the feasibility of decomposing a matrix into three component matrices for image recovery. Image recovery emerges in many areas, such as image processing, computer vision, and pattern recognition. Recently, the low rank assumption-based image recovery methods catch the researcher's attention. The authors assume the real data matrix has low rank and the error matrix is sparse. However, they are limited to the low-rank component being exactly low-rank, and the sparse component being exactly sparse. Either or both these assumptions are not exactly satisfied in practice and should be relaxed. This Letter presents a tri-decomposition method for dealing with the image data corrupted by both large sparse noise and small dense noise. The method parts the observed data into the clean data, sparse noise, and dense noise by different measure functions. Extensive experiments on face images and surveillance videos demonstrate the effectiveness of the proposed method.
Starting Page	1322
Ending Page	1324
Page Count	3
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 23, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/23
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2018.5718
Journal	Electronics Letters
Publisher Date	2018-10-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Component Matrices Computer Vision Computer Vision And Image Processing Technique Data Matrix Error Matrix Face Image Image Data Image Processing Low Rank Assumption-based Image Recovery Method Low-rank Component Matrix Decomposition Optical, Image And Video Signal Processing Sparse Component Sparse Matrix Sparse Noise Tri-decomposition Method
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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