The blind image deconvolution techniques with sparsity prior in gradient domain are sensitive to noise, even a small amount of noise. To address this roblem, in this letter, we propose a novel blind deconvolution model that combines lowrank property, nonlocal similarity, and l0 sparsity prior. Low-rank property akes the proposed deblurring model robust to image noise. The joint utilization of nonlocal similarity and l0 sparsity prior has improved the accuracy of blur kernel estimation and restor
