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Robust video denoising using low rank matrix completion
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.
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Citations
Cites
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
Cited by
Weighted Nuclear Norm Minimization with Application to Image Denoising
Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.366 · Cited by 2,316
Hyperspectral Image Restoration Using Low-Rank Matrix Recovery
Zhang, H., He, W., Zhang, L., Shen, H., & Yuan, Q. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2284280 · Cited by 887
Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization
Hu, Y., Zhang, D., Ye, J., Li, X., & He, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.271 · Cited by 829
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
Peng, Y., Ganesh, A., Wright, J., Xu, W., & Ma, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.282 · Cited by 817
Group-Based Sparse Representation for Image Restoration
Zhang, J., Zhao, D., & Gao, W. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2323127 · Cited by 768
Metadata
- Authors
- Ji, H., Liu, C., Shen, Z., & Xu, Y.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 1791-1798
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2109240917
- Cited by
- 483
- References
- 1
- FWCI
- 19.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ji, H., Liu, C., Shen, Z., & Xu, Y. (2010). Robust video denoising using low rank matrix completion. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2010.5539849
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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