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Robust L1 PCA and Its Application in Image Denoising |
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Editor(s): Zhang T, Nardell CA, Smith DD, Lu H |
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International Society for Optical Engineering (SPIE) |
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Washington, United States of America |
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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. |
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Proceedings of MIPPR 2007: Automatic Target Recognition and Image Analysis and Multispectral Image Acquisition, v.6786 (67860T) |
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