L_0 -Regularization Based on Sparse Prior for Image Deblurring.

In this paper we propose a novel (L_0) penalty function of both gradient and image itself as the regular term in the total energy function. This regular term is based on sparse prior and solved as part of mathematical optimization problem. Our method not only reserves structure information of the image but also avoids over smooth in the final restoration. We illustrate the applicability and validity of our method through experiments on both synthetic and natural blurry images. Despite we don’t have numerous iterations, the convergence rate and result quality outperform the most state-of-the-art methods.