Open Access
2014 Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion
Xu Han, Jiasong Wu, Lu Wang, Yang Chen, Lotfi Senhadji, Huazhong Shu
Abstr. Appl. Anal. 2014(SI64): 1-8 (2014). DOI: 10.1155/2014/765782


Matrix completion that estimates missing values in visual data is an important topic in computer vision. Most of the recent studies focused on the low rank matrix approximation via the nuclear norm. However, the visual data, such as images, is rich in texture which may not be well approximated by low rank constraint. In this paper, we propose a novel matrix completion method, which combines the nuclear norm with the local geometric regularizer to solve the problem of matrix completion for redundant texture images. And in this paper we mainly consider one of the most commonly graph regularized parameters: the total variation norm which is a widely used measure for enforcing intensity continuity and recovering a piecewise smooth image. The experimental results show that the encouraging results can be obtained by the proposed method on real texture images compared to the state-of-the-art methods.


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Xu Han. Jiasong Wu. Lu Wang. Yang Chen. Lotfi Senhadji. Huazhong Shu. "Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion." Abstr. Appl. Anal. 2014 (SI64) 1 - 8, 2014.


Published: 2014
First available in Project Euclid: 27 February 2015

zbMATH: 07023041
MathSciNet: MR3216075
Digital Object Identifier: 10.1155/2014/765782

Rights: Copyright © 2014 Hindawi

Vol.2014 • No. SI64 • 2014
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