  Blind image deconvolution is the problem of recovering the latent image from
the only observed blurry image when the blur kernel is unknown. In this paper,
we propose an edge-based blur kernel estimation method for blind motion
deconvolution. In our previous work, we incorporate both sparse representation
and self-similarity of image patches as priors into our blind deconvolution
model to regularize the recovery of the latent image. Since almost any natural
image has properties of sparsity and multi-scale self-similarity, we construct
a sparsity regularizer and a cross-scale non-local regularizer based on our
patch priors. It has been observed that our regularizers often favor sharp
images over blurry ones only for image patches of the salient edges and thus we
define an edge mask to locate salient edges that we want to apply our
regularizers. Experimental results on both simulated and real blurry images
demonstrate that our method outperforms existing state-of-the-art blind
deblurring methods even for handling of very large blurs, thanks to the use of
the edge mask.

Computer Vision and Pattern Recognition