A local blurred image restoration method based on the coded exposure and motion prior information was proposed, aiming at the problem of ill-posed and background damage along with the local motion blurred image restoration process. The theoretical model of coded exposure imaging was analyzed, and the fitness function criterion for the optimum code sequence selection was established. Through the object-image relationship, the preliminary estimation scale parameters of point spread function (PSF) for moving target were obtained as the prior information of motion. The background difference subtraction method was used to extract the motion blurred target area accurately by synthesizing the overlapping characteristics of the motion blurred image by coded exposure. Combined with the prior information, the student-t restoration algorithm based on the Bayesian maximum posteriori probability framework was introduced to estimate and reconstruct the PSF accurately. And the restoration results were obtained by several iterations fast. The experimental simulation system was built and the reconstruct experiments aming at the real motion targets were carried out. Experimental results show that the method can improve the pathological problem of motion blur restoration in traditional exposure effectively, as well as restrain the magnification effect of edge ringing and background noise in the restoration process. The images restored by the proposed method have better subjective and objective evaluation results than that of other methods.