Hyper-Laplacian Regularized Non-Local Low-Rank Prior for Blind Image Deblurring

Blind deblurring of single image is a challenging image restoration problem. Recent various image priors have been successfully explored to solve this ill-posed problem. In this paper, based on the non-local self-similarity, we propose a novel method for blind image deblurring, which can simultaneously capture the intrinsic structure correlation and spatial sparsity of an image. Specifically, we use the hyper-Laplace prior to model the structure information of non-local similar patches, and embed it into the low-rank model as a smooth term of the energy equation. Since the established energy function is non-convex, an effective iterative optimization scheme is designed to effectively implement the proposed algorithm. In addition, we evaluate the proposed method for non-uniform deblurring problem. Extensive experimental results on both synthetic and real-world images show that the proposed method performs competitively against the state-of-the-art methods.