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	JOINT IMAGE DENOISING USING SELF-SIMILARITY BASED LOW-RANK APPROXIMATIONS
	Zhang, Yongqin ; Liu, Jiaying ; Yang, Saboya ; Guo, Zongming
	2013
关键词	Dimensionality reduction parallel analysis eigenvalue decomposition low-rank approximation NONLOCAL MEANS DICTIONARIES
英文摘要	The observed images are usually noisy due to data acquisition and transmission process. Therefore, image denoising is a necessary procedure prior to post-processing applications. The proposed algorithm exploits the self-similarity based low rank technique to approximate the real-world image in the multivariate analysis sense. It consists of two successive steps: adaptive dimensionality reduction of similar patch groups, and the collaborative filtering. For each target patch, the singular value decomposition (SVD) is used to factorize the similar patch group collected in a local search window by block-matching. Parallel analysis automatically selects the principal signal components by discarding the nonsignificant singular values. After the inverse SVD transform, the denoised image is reconstructed by the weighted averaging approach. Finally, the collaborative Wiener filtering is applied to further remove the noise. Experimental results show that the proposed algorithm surpasses the state-of-the-art methods in most cases.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000335493500080&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; Imaging Science & Photographic Technology; Telecommunications; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1109/VCIP.2013.6706404
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/321155]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhang, Yongqin,Liu, Jiaying,Yang, Saboya,et al. JOINT IMAGE DENOISING USING SELF-SIMILARITY BASED LOW-RANK APPROXIMATIONS. 2013-01-01.

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