Video Super-resolution using Edge-based Optical Flow and Intensity Prediction

Full-image based motion prediction is widely used in video super-resolution (VSR) that results outstanding outputs with arbitrary scenes but costs huge time complexity. In this paper, we propose an edge-based motion and intensity prediction scheme to reduce the computation cost while maintain good enough quality simultaneously. The key point of reducing computation cost is to focus on extracted edges rather than the whole frame when finding motion vectors (optical flow) of the video sequence in accordance with human vision system (HVS). Bi-directional optical flow is usually adopted to increase the prediction accuracy but it also increase the computation time. Here we propose to obtain the backward flow from foregoing forward flow prediction which effectively save the heavy load. We perform a series of experiments and comparisons between existed VSR methods and our proposed edge-based method with different sequences and upscaling factors. The results reveal that our proposed scheme can successfully keep the super-resolved sequence quality and get about 4x speed up in computation time.