During the image restoration, choosing a proper model that describes the image is the key of restoration. A MAP framework based on Gibbs to extract a single super resolution image from image sequences is proposed. Additional novel observation data can be obtained and the feasible solution space is constrained with apriori assumptions on the form of the solution. Experimental results are provided to illustrate the performance of the algorithm that is better and faster than others.