CASIA OpenIR  > 精密感知与控制研究中心  > 人工智能与机器学习
Image denoising via nonlocally sparse coding and tensor decomposition
Wenrui Hu; Yuan Xie; Wensheng Zhang; Limin Zhu; Yanyun Qu; Yuanhua Tan
2014
Conference Name6th International Conference on Internet Multimedia Computing and Service, ICIMCS 2014
Source PublicationACM International Conference Proceeding Series, 2014:283-288
Conference DateJuly 10, 2014 - July 12, 2014
Conference PlaceXiamen, China
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.
KeywordCollaborative Filtering Sparse Coding Ttensor Decomposition Bregman Iteration
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12261
Collection精密感知与控制研究中心_人工智能与机器学习
Corresponding AuthorWensheng Zhang
Recommended Citation
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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