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dc.contributor.authorHu, Wenruizh_CN
dc.contributor.authorXie, Yuanzh_CN
dc.contributor.authorZhang, Wenshengzh_CN
dc.contributor.authorZhu, Liminzh_CN
dc.contributor.authorQu, Yanyunzh_CN
dc.contributor.authorTan, Yuanhuazh_CN
dc.contributor.author曲延云zh_CN
dc.date.accessioned2015-07-22T02:40:03Z
dc.date.available2015-07-22T02:40:03Z
dc.date.issued2014zh_CN
dc.identifier.citationACM International Conference Proceeding Series, 2014:283-288zh_CN
dc.identifier.other20143318062243zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86953
dc.descriptionConference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.zh_CN
dc.descriptionNational Natural Foundation of China; SIGMM China Chapter; Xiamen Universityzh_CN
dc.description.abstractThe nonlocally sparse coding and collaborative filtering techniques have been proved very effective in image denoising, which yielded state-of-the-art performance at this time. In this paper, the two approaches are adaptively embedded into a Bayesian framework to perform denoising based on split Bregman iteration. In the proposed framework, a noise-free structure part of the latent image and a refined observation with less noise than the original observation are mixed as constraints to finely remove noise iteration by iteration. To reconstruct the structure part, we utilize the sparse coding method based on the proposed nonlocally orthogonal matching pursuit algorithm (NLOMP), which can improve the robustness and accuracy of sparse coding in present of noise. To get the refined observation, the collaborative filtering method are used based on Tucker tensor decomposition, which can takes full advantage of the multilinear data analysis. Experiments illustrate that the proposed denoising algorithm achieves highly competitive performance to the leading algorithms such as BM3D and NCSR. Copyright 2014 ACM.zh_CN
dc.language.isoen_USzh_CN
dc.publisherAssociation for Computing Machineryzh_CN
dc.source.urihttp://dx.doi.org/10.1145/2632856.2632888zh_CN
dc.subjectAlgorithmszh_CN
dc.subjectCodes (symbols)zh_CN
dc.subjectCollaborative filteringzh_CN
dc.subjectImage denoisingzh_CN
dc.subjectInternetzh_CN
dc.subjectTensorszh_CN
dc.titleImage denoising via nonlocally sparse coding and tensor decompositionzh_CN
dc.typeConferencezh_CN


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