Robust L1 PCA and application in image denoising

Junbin Gao; Paul W. H. Kwan; Yi Guo

doi:10.1117/12.774719

15 November 2007 Robust L1 PCA and application in image denoising

Junbin Gao, Paul W. H. Kwan, Yi Guo

Author Affiliations +

Proceedings Volume 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition; 67860T (2007) https://doi.org/10.1117/12.774719
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

The so-called robust L1 PCA was introduced in our recent work [1] based on the L1 noise assumption. Due to the heavy tail characteristics of the L1 distribution, the proposed model has been proved much more robust against data outliers. In this paper, we further demonstrate how the learned robust L1 PCA model can be used to denoise image data.

Citation Download Citation

Junbin Gao, Paul W. H. Kwan, and Yi Guo "Robust L1 PCA and application in image denoising", Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67860T (15 November 2007); https://doi.org/10.1117/12.774719

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