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Blind Deconvolution With Nonlocal Similarity and l(0) Sparsity for Noisy Image
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第一作者: Ren WH(任卫红);Tian JD(田建东);Tang YD(唐延东)
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发表年度: 2016
期: 4
卷: 23
页: 439-443
摘要: 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(0) sparsity prior. Low-rank property makes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l(0) 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.
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刊物名称: IEEE SIGNAL PROCESSING LETTERS
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