Bayesian motion blur identification using blur priori

This paper presents a new approach to motion blur identification based on Bayesian paradigm and maximum a posteriori (MAP) method. To represent general spatial-invariant motion blurs, especially those caused by nonuniform and nonstraight motions, a new blur model is proposed by incorporating the knowledge of blur type or partially known motion shape as a blur priori. Based on this model, an iterative method is adopted for the MAP blur identification. Experimental result shows that the proposed method offers an efficient way to precisely estimate the motion blur and to generate better restoration result, especially for the widely existing motion blurs caused by complicated motions and cannot be simplified as straight-and-uniform motion blurs.