Abstract
Deblurring form a single blurred image is a challenge task in computer vision. It is an ill-posed problem to estimate the unknown blur kernel and recover the original image. There are many significant deblurring methods toward the natural images; however, few of them are not able to perform well on face images. Based on L_0 norm prior, we propose a two-step method for the images deblurring. The proposed method does not require any facial dataset to initialize the gradient of contours or any complex filtering strategies. In first step, we combine L_0 norm prior with our local smooth prior to predict the blur kernel. With simple Gaussian filtering, we could maintain the smooth region in the sharp image. In second step, refine the previous kernel result. In order to discard low intensity pixels (seemed to be noises) on kernel, we impose the sparsity on the kernel with L_0 norm regularization. Experimental results demonstrate that our proposed algorithm perform well on the facial images.