# VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge

arXiv: https://arxiv.org/abs/2003.02115 · HTML: https://mlsanity.com/paper/2003.02115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2020.

## Built on by (most cited first, 3 of 3)

- [Learning for Unconstrained Space-Time Video Super-Resolution](https://mlsanity.com/paper/2102.13011/) (2021)
- [Multi-Grid Back-Projection Networks](https://mlsanity.com/paper/2101.00150/) (2021)
- [GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution](https://mlsanity.com/paper/2505.10577/) (2025)

## Abstract (arXiv)

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.