Blind Deconvolution with Non-local Similarity and $l_0$ Sparsity for Noisy Image

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 $l_0$ 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.