Image denoising via nonlocally sparse coding and tensor decomposition

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 人工智能与机器学习（杨雪冰）-技术团队

	Image denoising via nonlocally sparse coding and tensor decomposition
	Wenrui Hu; Yuan Xie; Wensheng Zhang; Limin Zhu; Yanyun Qu; Yuanhua Tan
	2014
会议名称	6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014
会议录名称	ACM International Conference Proceeding Series, 2014:283-288
会议日期	July 10, 2014 - July 12, 2014
会议地点	Xiamen, China
摘要	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.
关键词	Collaborative Filtering Sparse Coding Ttensor Decomposition Bregman Iteration
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12261
专题	多模态人工智能系统全国重点实验室_人工智能与机器学习（杨雪冰）-技术团队
通讯作者	Wensheng Zhang
推荐引用方式 GB/T 7714	Wenrui Hu,Yuan Xie,Wensheng Zhang,et al. Image denoising via nonlocally sparse coding and tensor decomposition[C],2014.

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