Blind deconvolution, a chronic inverse problem, is the recovery of the latent sharp image from a blurred one when the blur kernel is unknown. Recent algorithms based on the MAP approach encounter failures since the global minimum of the negative MAP scores really favors the blurry image. The goal of this paper is to demonstrate that the sharp image can be obtained from the local minimum by using the MAP approach. We first propose a cross-scale constraint to make the sharp image correspond to a good local minimum. Then the cross-scale initialization, iterative likelihood update and the iterative residual deconvolution are adopted to trap the MAP approach in the desired local minimum. These techniques result in our cross-scale blind deconvolution approach which constrains the solution from coarse to fine. We test our approach on the standard dataset and many other challenging images. The experimental results suggest that our approach outperforms all existing alternatives.
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