A Fast Blind Spatially-Varying Motion Deblurring Algorithm with Camera Poses Estimation

Most existing non-uniform deblurring algorithms model the blurry image as a weighted summation of several sharp images which are warped by one latent image with different homographies. These algorithms usually suffer from high computational cost due to the huge number of homographies to be considered. In order to solve this problem, we introduce a novel single image deblurring algorithm to remove the spatially-varying blur. Since the real motion blur kernel is very sparse, in this paper we first estimate a feasible active set of homographies which may hold large weights in the blur kernel and then compute the corresponding weights on these homographies to reconstruct the blur kernel. Since the size of the active set is quite small, the deblurring algorithm will become much faster. Experiment results show that the proposed algorithm can effectively and efficiently remove the non-uniform blur caused by camera shake.