Blind image deblurring using adaptive priors

Abstract

For blind image deblurring, a good prior knowledge can guide the maximum a posterior (MAP) based algorithms to be away from the trivial solution. Therefore, many existing methods focus on designing effective priors to constrain the solution space. However, blind deconvolution with fixed priors is not robust. And many priors are extremely costly to design and compute. In this paper, we proposed a blind deconvolution method with adaptive priors under the MAP framework. Specifically, we carry out our algorithm under the multi-scale, and at each scale we add specific sparse regularization to standard deblurring formulation. By tunning both the priors and the weights we can give more flexible sparse regularization constraint. After iteration, our algorithm output both latent image and estimated blur kernel, simultaneously. We prove the convergence of the proposed algorithm. Extensive experiments show the effectiveness of our proposed approach.