A robust super resolution method for video

Super resolution reconstruction produces a higher resolution image based on a set of low resolution images, taken from the same scene. Recently, many papers have been published, proposing a variety algorithms of video super resolution. This paper presents a new approach to video super resolution, based on sparse coding and belief propagation. First, find the candidate pixels on multiple frames using sparse coding and belief propagation. Second, exploit the similarities of candidate pixels using the Non-local Means method to average out the noise among similar patches. The experimental results show the effectiveness of our method and demonstrate its robustness to other super resolution methods.