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• W1589819097 startingPage "4148" @default.
• W1589819097 abstract "In this paper, we propose a novel model, a discriminatively learned iterative shrinkage (DLIS) model, for color image denoising. The DLIS is a generalization of wavelet shrinkage by iteratively performing shrinkage over patch groups and whole image aggregation. We discriminatively learn the shrinkage functions and basis from the training pairs of noisy/noise-free images, which can adaptively handle different noise characteristics in luminance/chrominance channels, and the unknown structured noise in real-captured color images. Furthermore, to remove the splotchy real color noises, we design a Laplacian pyramid-based denoising framework to progressively recover the clean image from the coarsest scale to the finest scale by the DLIS model learned from the real color noises. Experiments show that our proposed approach can achieve the state-of-the-art denoising results on both synthetic denoising benchmark and real-captured color images." @default.
• W1589819097 created "2016-06-24" @default.
• W1589819097 creator A5037419606 @default.
• W1589819097 creator A5087487533 @default.
• W1589819097 date "2015-11-01" @default.
• W1589819097 modified "2023-10-17" @default.
• W1589819097 title "Color Image Denoising via Discriminatively Learned Iterative Shrinkage" @default.
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• W1589819097 doi "https://doi.org/10.1109/tip.2015.2448352" @default.
• W1589819097 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26111391" @default.
• W1589819097 hasPublicationYear "2015" @default.
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