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	CONTENT-ADAPTIVE LOW RANK REGULARIZATION FOR IMAGE DENOISING
	Liu, Hangfan ; Zhang, Xinfeng ; Xiong, Ruiqin
	2016
关键词	Non-local similarity low-rank empirical Bayes image denoising LEARNED DICTIONARIES TRANSFORM-DOMAIN RESTORATION SPARSE COMPRESSION
英文摘要	Prior knowledge plays an important role in image denoising tasks. This paper utilizes the data of the input image to adaptively model the prior distribution. The proposed scheme is based on the observation that, for a natural image, a matrix consisted of its vectorized non-local similar patches is of low rank. We use a non-convex smooth surrogate for the low rank regularization, and view the optimization problem from the empirical Bayesian perspective. In such framework, a parameter-free distribution prior is derived from the grouped non-local similar image contents. Experimental results show that the proposed approach is highly competitive with several state-of-art denoising methods in PSNR and visual quality.; CPCI-S(ISTP); liuhf@pku.edu.cn; xfzhang@ntu.edu.sg; rqxiong@pku.edu.cn; 3091-3095
语种	英语
出处	23rd IEEE International Conference on Image Processing (ICIP)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/459887]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Liu, Hangfan,Zhang, Xinfeng,Xiong, Ruiqin. CONTENT-ADAPTIVE LOW RANK REGULARIZATION FOR IMAGE DENOISING. 2016-01-01.

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