Blind image deblurring based on sparse prior of dictionary pair

Blind image deblurring, aiming at obtaining the sharp image from blurred one, is a widely existing problem in image processing. Traditional image deblurring methods always use the deconvolution method to remove the blur kernel´s effect, however, deconvolution is so sensitive to noise that inevitable artifacts always exist in the deblurring results, even though regularity terms are introduced as constraints. In this paper, we propose a novel blind image deblurring method based on the sparse prior of dictionary pair, estimating the sparse coefficient, sharp image and blur kernel alternately. The proposed method could avoid the deconvolution problem which is an ill-posed problem, and obtain the result with fewer artifacts. Compared with the state-of-the-art method, experimental results demonstrate that the proposed method could obtain better performance.