Blind Motion Deblurring Based on Fused l0-l1 Regularization

In blind motion deblurring, various regularization models using different priors of either the image or the blur kernel are proposed to estimate the blur kernel, with tendency towards naive \(\ell _0\) norm or its approximations. In this paper, we propose a novel fused \(\ell _0\)-\(\ell _1\) regularization approach to estimate the motion blur kernel by imposing sparsity and continuation properties on natural images. A fast numerical scheme is then deduced by coupling operator splitting and the Augmented Lagrangian method to solve the proposed model efficiently. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed method and the superiority over the state-of-the-art methods.