In this paper, we are interested in the problem of motion-blurred image blind restoration. A new method for this ill-posed problem is proposed. We present an adaptive Huber Markov Random Field (HMRF) image prior model as the regularization term, which can be suitable for motion-blurred situation, then turn the ill-posed problem to well-posed. It can preserve fine image details and edges. However, image processing always represents as high-dimension equations that are complicated and computationally expensive for stable solutions. To this point, we propose a combinatorial optimization method benefited from variable substitution optimization technique and Tikhonov regularization technique. These two methods implement alternately in frequency domain, hence optimal original image and point spread function (PSF) can be obtained respectively. Some experiments are presented by comparing the proposed method among classical Wiener filter, Richardson-Lucy deconvolution and a state-of-the art method. These experiments demonstrate the advantages of the proposed priori and combinatorial optimization method.
['Yiping Wang', 'Xinsheng Huang', 'Yuzhuang Yan', 'Yongbin Zhen']
A New Method for Motion-Blurred Image Blind Restoration Based on Huber Markov Random Field