Robust Video Restoration by Joint Sparse and Low Rank Matrix Approximation

This paper presents a new patch-based video restoration scheme. By grouping similar patches in the spatiotemporal domain, we formulate the video restoration problem as a joint sparse and low-rank matrix approximation problem. The resulting nuclear norm and $\ell_1$ norm related minimization problem...

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Bibliographic Details
Published inSIAM journal on imaging sciences Vol. 4; no. 4; pp. 1122 - 1142
Main Authors Ji, Hui, Huang, Sibin, Shen, Zuowei, Xu, Yuhong
Format Journal Article
LanguageEnglish
Published Philadelphia Society for Industrial and Applied Mathematics 01.01.2011
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Summary:This paper presents a new patch-based video restoration scheme. By grouping similar patches in the spatiotemporal domain, we formulate the video restoration problem as a joint sparse and low-rank matrix approximation problem. The resulting nuclear norm and $\ell_1$ norm related minimization problem can also be efficiently solved by many recently developed numerical methods. The effectiveness of the proposed video restoration scheme is illustrated on two applications: video denoising in the presence of random-valued noise, and video in-painting for archived films. The numerical experiments indicate that the proposed video restoration method compares favorably against many existing algorithms. [PUBLICATION ABSTRACT]
ISSN:1936-4954
1936-4954
DOI:10.1137/100817206