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• W2564805107 abstract "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." @default.
• W2564805107 created "2017-01-06" @default.
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• W2564805107 date "2017-07-01" @default.
• W2564805107 modified "2023-10-16" @default.
• W2564805107 title "Structure-Based Low-Rank Model With Graph Nuclear Norm Regularization for Noise Removal" @default.
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• W2564805107 doi "https://doi.org/10.1109/tip.2016.2639781" @default.
• W2564805107 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28113320" @default.
• W2564805107 hasPublicationYear "2017" @default.
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