Blur estimation and superresolution from multiple registered images

In this study, a superresolution method using registered, noisy and down sampled images is presented. Maximum a Posteriori (MAP) method, one of the statistical pixel domain approaches, is used as the superresolution algorithm. Performances of different data fidelity terms and regularization terms used in the literature are shown. In most of the applications the effects that degrade the image frame are assumed to be known completely or known limited. In this application, the performances of the several methods used to find the amount of blur caused by the unfocussed camera lenses are shown and the best method results are used in the superrresolution algorithm. In this way, the error value of superresolved image is decreased.