Image Denoising via Bandwise Adaptive Modeling and Regularization   Exploiting Nonlocal Similarity

doi:10.1109/TIP.2016.2614160

CORC > 北京大学 > 信息科学技术学院

	Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity
	Xiong, Ruiqin ; Liu, Hangfan ; Zhang, Xinfeng ; Zhang, Jian ; Ma, Siwei ; Wu, Feng ; Gao, Wen
刊名	IEEE TRANSACTIONS ON IMAGE PROCESSING
	2016
关键词	Image denoising transform domain modeling bandwise modeling adaptive regularization adaptive soft-thresholding nonlocal similarity GAUSSIAN MIXTURE-MODELS SPARSE REPRESENTATION LEARNED DICTIONARIES TRANSFORM-DOMAIN RESTORATION NOISE RECONSTRUCTION COMPRESSION ESTIMATORS REGRESSION
DOI	10.1109/TIP.2016.2614160
英文摘要	This paper proposes a new image denoising algorithm based on adaptive signal modeling and regularization. It improves the quality of images by regularizing each image patch using bandwise distribution modeling in transform domain. Instead of using a global model for all the patches in an image, it employs content-dependent adaptive models to address the non-stationarity of image signals and also the diversity among different transform bands. The distribution model is adaptively estimated for each patch individually. It varies from one patch location to another and also varies for different bands. In particular, we consider the estimated distribution to have non-zero expectation. To estimate the expectation and variance parameters for every band of a particular patch, we exploit the nonlocal correlation in image to collect a set of highly similar patches as the data samples to form the distribution. Irrelevant patches are excluded so that such adaptively learned model is more accurate than a global one. The image is ultimately restored via bandwise adaptive soft-thresholding, based on a Laplacian approximation of the distribution of similar-patch group transform coefficients. Experimental results demonstrate that the proposed scheme outperforms several state-of-the-art denoising methods in both the objective and the perceptual qualities.; National Basic Research Program of China [2015CB351800]; National Natural Science Foundation of China [61370114, 61425026, 61421062]; China Postdoctoral Science Foundation [2016T90017]; Cooperative Medianet Innovation Center; SCI(E); ARTICLE; rqxiong@pku.edu.cn; liuhf@pku.edu.cn; xfzhang@ntu.edu.sg; jian.zhang@pku.edu.cn; swma@pku.edu.cn; fengwu@ustc.edu.cn; wgao@pku.edu.cn; 12; 5793-5805; 25
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/458314]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xiong, Ruiqin,Liu, Hangfan,Zhang, Xinfeng,et al. Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016.
APA	Xiong, Ruiqin.,Liu, Hangfan.,Zhang, Xinfeng.,Zhang, Jian.,Ma, Siwei.,...&Gao, Wen.(2016).Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity.IEEE TRANSACTIONS ON IMAGE PROCESSING.
MLA	Xiong, Ruiqin,et al."Image Denoising via Bandwise Adaptive Modeling and Regularization Exploiting Nonlocal Similarity".IEEE TRANSACTIONS ON IMAGE PROCESSING (2016).