Simultaneous super-resolution for video sequences

In this work, we propose a new super-resolution algorithm to simultaneously estimate all frames of a video sequence. The new algorithm is based on the Bayesian maximum a posteriori estimation. In contrast to other multi-frame super-resolution algorithms, the proposed algorithm does not include the motion in the observation model. Instead, transformations caused by the motion are used as a prior information in order to achieve smoothness in the motion trajectory. The proposed algorithm provides lower computational complexity than the traditional super-resolution algorithms when several frames need to be restored. The new algorithm is also robust to motion errors and outliers. We provide results to illustrate the superiority of the new method.