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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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https://www.hal.inserm.fr/inserm-01101215
Contributor : Laurent Jonchère <>
Submitted on : Tuesday, May 26, 2015 - 1:24:19 PM
Last modification on : Friday, July 5, 2019 - 10:16:02 AM
Long-term archiving on: : Tuesday, September 15, 2015 - 6:53:31 AM

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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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