Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2010.5539849
Title: Robust video denoising using Low rank matrix completion
Authors: Ji, H. 
Liu, C. 
Shen, Z. 
Xu, Y. 
Issue Date: 2010
Source: Ji, H., Liu, C., Shen, Z., Xu, Y. (2010). Robust video denoising using Low rank matrix completion. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 1791-1798. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5539849
Abstract: Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms. ©2010 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/115484
ISBN: 9781424469840
ISSN: 10636919
DOI: 10.1109/CVPR.2010.5539849
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