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	Image denoising via nonlocally sparse coding and tensor decomposition
	Hu, Wenrui ; Xie, Yuan ; Zhang, Wensheng ; Zhu, Limin ; Qu, Yanyun ; Tan, Yuanhua ; Qu YY(曲延云)
	2014
关键词	Algorithms Codes (symbols) Collaborative filtering Image denoising Internet Tensors
英文摘要	Conference Name:6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014. Conference Address: Xiamen, China. Time:July 10, 2014 - July 12, 2014.; National Natural Foundation of China; SIGMM China Chapter; Xiamen University; The 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.
语种	英语
出处	http://dx.doi.org/10.1145/2632856.2632888
出版者	Association for Computing Machinery
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86953]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Hu, Wenrui,Xie, Yuan,Zhang, Wensheng,et al. Image denoising via nonlocally sparse coding and tensor decomposition. 2014-01-01.

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