Scene-based video super-resolution with minimum mean square error estimation

Motion estimation is a key problem in video super-resolution (SR). If the estimation is highly accurate then the high resolution (HR) frames reconstructed is better in quality. Otherwise with small errors in estimation, they will create more degradation in the reconstructed HR frames. In many recent studies, the motion estimation is applied on every block of pixels. There is too little input data for estimating process so that it is hard to get high accuracy in results. This paper presents a new method for SR video image reconstruction through two main ideas. First, video frames are separated into two sections, as scene and motive objects. The motions of the scene are the same and uniform. We will have much data for estimating, so that the result can be more accurate. Second, the motion estimation is based on three parameters, rotation and shifts in vertical and horizontal. It presents a perfectly estimating for real motion of camera when capturing video frames. Based on that, an efficient algorithm is proposed by combination of block matching search method and minimum mean square error estimation. The results of the proposed algorithm are more accurate than those of other recent algorithms. It can be easy to see one we visualize the HR video frames reconstructed by other algorithms.