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• W2334453399 abstract "The blind image deconvolution techniques with sparsity prior in gradient domain are sensitive to noise, even a small amount of noise. To address this problem, in this letter, we propose a novel blind deconvolution model that combines low-rank property, nonlocal similarity, and l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> sparsity prior. Low-rank property makes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> sparsity prior has improved the accuracy of blur kernel estimation and restores the fine image details. A numerical method is also given to solve the proposed problem. Experimental results on synthetic and real data show that our algorithm performs better against with the state-of-the-art methods for both noise and noise-free images." @default.
• W2334453399 created "2016-06-24" @default.
• W2334453399 creator A5061465385 @default.
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• W2334453399 date "2016-01-01" @default.
• W2334453399 modified "2023-10-17" @default.
• W2334453399 title "Blind Deconvolution with Non-local Similarity and $l_0$ Sparsity for Noisy Image" @default.
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• W2334453399 doi "https://doi.org/10.1109/lsp.2016.2530855" @default.
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