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Proceedings Paper

Robust L1 PCA and application in image denoising
Author(s): Junbin Gao; Paul W. H. Kwan; Yi Guo
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Paper 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.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6786, MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, 67860T (15 November 2007); doi: 10.1117/12.774719
Show Author Affiliations
Junbin Gao, Charles Sturt Univ. (Australia)
Paul W. H. Kwan, Univ. of New England (Australia)
Yi Guo, Univ. of New England (Australia)


Published in SPIE Proceedings Vol. 6786:
MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition

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