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• W3010015985 abstract "It is challenging to simultaneously achieve noise suppression and fine detail preservation in noisy image fusion. To address this challenge, we propose a novel strategy for noisy image fusion. Assuming that an image can be modeled as a superposition of low-rank and sparse (LR-and-S) components, we develop a novel discriminative dictionary learning algorithm to construct two dictionaries so as to decompose the input image into LR-and-S components. Specifically, to make dictionary possess discriminative power, we enforce spatial morphology constraint on each dictionary. Furthermore, we develop within-class consistency constraint by exploiting the similarity of low-rank components and impose it on the coding coefficients to further improve the discriminative power of the learned dictionary. In image decomposition, external patch prior and internal self-similarity prior of an image are exploited to build image decomposition model, based on which the latent subspace for fusion and recovery is estimated by minimizing rank-regularization of the subspace learned via clustering of similar patches. To construct different components of fused result, we use l1-norm maximization rule to fuse the decomposed components. Finally, the fused image is obtained by adding the fused components together. Experiments demonstrate that our method outperforms several state-of-the-art methods in terms of both objective quality assessment and subjective visual perception." @default.
• W3010015985 created "2020-03-13" @default.
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• W3010015985 date "2020-06-01" @default.
• W3010015985 modified "2023-10-14" @default.
• W3010015985 title "Noise-robust image fusion with low-rank sparse decomposition guided by external patch prior" @default.
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• W3010015985 doi "https://doi.org/10.1016/j.ins.2020.03.009" @default.
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