Learning priors for super-resolution in video sequence

Video becomes a crucial information resource in last decades, because of the rapid development of camera as well as the internet explosion. High-quality video sequences are always desired in lots of fields. Since the bottleneck of data storage and interferences of shooting condition, we cannot always obtain high-resolution video. This botheration can be circumvented by super-resolution. Currently, almost super-resolution techniques are in the framework of Maximum a Posterior (MAP). Appropriate parameters of prior distribution are crucial for recovering accurate super-resolution image. We utilise a novel Weighted Cross Validation (WCG) method to learn theses prior parameters. Comparison experiments are provided to illustrate the effectiveness of our approach.