Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/5808
Title: Robust L1 PCA and Its Application in Image Denoising
Contributor(s): Gao, Junbin (author); Kwan, Paul Hing  (author); Guo, Yi (author)
Publication Date: 2007
DOI: 10.1117/12.774719
Handle Link: https://hdl.handle.net/1959.11/5808
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.
Publication Type: Conference Publication
Conference Name: MIPPR 2007: Automatic Target Recognition and Image Analysis and Multispectral Image Acquisition, Wuhan, China, 15-17 November, 2007
Conference Details: MIPPR 2007: Automatic Target Recognition and Image Analysis and Multispectral Image Acquisition, Wuhan, China, 15-17 November, 2007
Source of Publication: Proceedings of MIPPR 2007: Automatic Target Recognition and Image Analysis and Multispectral Image Acquisition, v.6786 (67860T)
Publisher: SPIE: International Society for Optical Engineering
Place of Publication: Washington, United States of America
Field of Research (FOR): 080109 Pattern Recognition and Data Mining
Socio-Economic Outcome Codes: 890201 Application Software Packages (excl. Computer Games)
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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