Blind image deblurring based on the sparsity of patch minimum information

Abstract Blind image deblurring is a very challenging inverse problem due to the severe ill-posedness caused by the unknown kernel and the latent clear image. To tackle this problem, appropriate smoothing regularizations and image priors are usually employed and incorporated into the associated variational models to alleviate the inherent ill-posedness. In this paper, we first propose a strongly imposed zero patch minimum constraint for the latent image, which helps alleviate the ill-posedness of the inverse problem for blind image deblurring. Then, we retrieve important fine details by assigning the patch minimum information obtained from the blurred image back to the latent image to further enhance its structure. Finally, we introduce an adaptive regularizer which was shown to have significantly better edge-preserving property than the total variation regularizer for the image restoration of degraded images. Operator splitting techniques are used to accomplish an efficient numerical implementation of the proposed variational model. A number of numerical experiments and comparisons with some state-of-the-art methods are conducted to demonstrate the effective performance of the newly proposed method.