Video super-resolution network using detail component extraction and optical flow enhancement algorithm.

The video super-resolution (SR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. Existing deep learning-based approaches usually utilize LR optical flow for video SR tasks. However, the accuracy of LR optical flow is not enough to recover the fine detail part. In this paper, we propose a video SR network that uses optical flow SR and optical flow enhancement algorithms to provide accurate temporal dependency. And extract the detail component of LR adjacent frames as supplementary information for accurate feature extraction. Firstly, the network infers HR optical flow from LR optical flow, and uses the optical flow enhancement algorithm to enhance HR optical flow. Then the processed HR optical flows are used as the input of the motion compensation network. Secondly, we extract detail component to reduce the error caused by motion compensation based on optical flow. Finally, the SR results are generated through the SR network. We perform comprehensive comparative experiments on two datasets: Vid4 and DAVIS. The results show that, compared with other state-of-the-art methods, the proposed video SR method achieves the better performance. [ABSTRACT FROM AUTHOR]