Blind Deconvolution With Nonlocal Similarity and l0 Sparsity for Noisy Image

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. Lo...

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Bibliographic Details
Published in:	IEEE signal processing letters Vol. 23; no. 4; pp. 439 - 443
Main Authors:	Weihong Ren, Jiandong Tian, Yandong Tang
Format:	Journal Article
Language:	English
Published:	IEEE 01-04-2016
Subjects:	Blind image deconvolution Cameras Deconvolution Estimation Image edge detection Image restoration Kernel l0 sparsity Low rank Noise measurement Non-local similarity l_0 sparsity low rank nonlocal similarity Blind image deconvolution
Online Access:	Get full text
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Summary:	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.
ISSN:	1070-9908 1558-2361
DOI:	10.1109/LSP.2016.2530855