Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal

Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 26(2017), 7 vom: 15. Juli, Seite 3098-3112
1. Verfasser: Ge, Qi (VerfasserIn)
Weitere Verfasser: Jing, Xiao-Yuan, Wu, Fei, Wei, Zhi-Hui, Xiao, Liang, Shao, Wen-Ze, Yue, Dong, Li, Hai-Bo
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2017
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
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245 1 0 |a Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal 
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520 |a Nonlocal image representation methods, including group-based sparse coding and block-matching 3-D filtering, have shown their great performance in application to low-level tasks. The nonlocal prior is extracted from each group consisting of patches with similar intensities. Grouping patches based on intensity similarity, however, gives rise to disturbance and inaccuracy in estimation of the true images. To address this problem, we propose a structure-based low-rank model with graph nuclear norm regularization. We exploit the local manifold structure inside a patch and group the patches by the distance metric of manifold structure. With the manifold structure information, a graph nuclear norm regularization is established and incorporated into a low-rank approximation model. We then prove that the graph-based regularization is equivalent to a weighted nuclear norm and the proposed model can be solved by a weighted singular-value thresholding algorithm. Extensive experiments on additive white Gaussian noise removal and mixed noise removal demonstrate that the proposed method achieves a better performance than several state-of-the-art algorithms 
650 4 |a Journal Article 
700 1 |a Jing, Xiao-Yuan  |e verfasserin  |4 aut 
700 1 |a Wu, Fei  |e verfasserin  |4 aut 
700 1 |a Wei, Zhi-Hui  |e verfasserin  |4 aut 
700 1 |a Xiao, Liang  |e verfasserin  |4 aut 
700 1 |a Shao, Wen-Ze  |e verfasserin  |4 aut 
700 1 |a Yue, Dong  |e verfasserin  |4 aut 
700 1 |a Li, Hai-Bo  |e verfasserin  |4 aut 
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