Manifold Area Prior for Blind Image Deblurring: A Continue Space Method

Ringing artifacts is a critical problem in image deblurring. Existing methods based on image sparsity cannot efficiently recognize the plausive edge and result in significant ringing artifacts. Those methods mostly consider the statistic prior for blur and clear images but ignore continuity of natural image in feature space. While, image intrinsic continuity could complement the sparsity information for image statistic prior. Intuitively, ringing effects should form the shake in feature space and break the piece-wise smoothness of natural image. To this end, we propose a minimizing manifold area prior (MAP) method to reduce the ringing artifacts. We first embed a two-dimension manifold into the image high-dimension feature space. Then the feature difference between blur and clear images could be described by manifold area, which could be represented via local Euclide-space. This is beneficial to proposing a fast and effective algorithm to calculate manifold area, which significantly improve the computational complexity. Experiments validate that our method is superior than state-of-the-arts in performance and time consuming, especially for ringing artifacts.