Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion

Abstract : 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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Article dans une revue
Abstract and Applied Analysis, Hindawi Publishing Corporation, 2014, 2014, pp.765782. 〈10.1155/2014/765782〉
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http://www.hal.inserm.fr/inserm-01101215
Contributeur : Laurent Jonchère <>
Soumis le : mardi 26 mai 2015 - 13:24:19
Dernière modification le : mardi 3 juillet 2018 - 10:58:04
Document(s) archivé(s) le : mardi 15 septembre 2015 - 06:53:31

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Xu Han, Jiasong Wu, Lu Wang, Yang Chen, Lotfi Senhadji, et al.. Linear Total Variation Approximate Regularized Nuclear Norm Optimization for Matrix Completion. Abstract and Applied Analysis, Hindawi Publishing Corporation, 2014, 2014, pp.765782. 〈10.1155/2014/765782〉. 〈inserm-01101215〉

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