This paper introduces VESR-Net, a method for video enhancement and super-resolution (VESR). We design a separate non-local module to explore the relations among video frames and fuse video frames efficiently, and a channel attention residual block to capture the relations among feature maps for video frame reconstruction in VESR-Net. We conduct experiments to analyze the effectiveness of these designs in VESR-Net, which demonstrates the advantages of VESR-Net over previous state-of-the-art VESR methods. It is worth to mention that among more than thousands of participants for Youku video enhancement and super-resolution (Youku-VESR) challenge, our proposed VESR-Net beat other competitive methods and ranked the first place.
@article{arxiv.2003.02115,
title = {VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge},
author = {Jiale Chen and Xu Tan and Chaowei Shan and Sen Liu and Zhibo Chen},
journal= {arXiv preprint arXiv:2003.02115},
year = {2020}
}