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|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  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||Statistics to Oct 2018:||Visitors: 132
|Appears in Collections:||Conference Publication|
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