An efficient approach based on Bayesian MAP for video super-resolution

Multi-frame super-resolution brings out much potential to reconstruct real high-resolution video sequences. This potential is achieved based on its capacity to combine missing information from different input low-resolution frames. Although there have been many studies in recent decades, super-resolution problems for real-world video processing still have many challenges. This is dues to two problems of: how to address the affecting factors: motion, sampling and noise explicitly and how to solve them exactly and efficiently. This paper introduces an efficient approach for video super-resolution by addressing real motion, sampling and noise models. Based on that, we proposed a model for receiving a practical video and an efficient framework to estimate adaptively the motion and noise to reconstruct the original high-resolution frames. Our system achieves promising results when compare with other state-of-the-art in quality and processing time.