UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
Yaofeng Xie1 Lingwei Kong2 Kai Chen2 Ziqiang Zheng3 Xiao Yu2 Zhibin Yu1,2,† Bing Zheng1,2
1College of Electronic Engineering,Ocean University of China
2Key Laboratory of Ocean Observation and Information of Hainan Province, Sanya Oceanographic Institution, Ocean University of China
3Department of Computer Science and Engineering,The Hong Kong University of Science and Technology
† corresponding author: yuzhibin@ouc.edu.cn; Project website: https://github.com/yzbouc/UVEB
This repository contains the official implementation and dataset of the CVPR2024 paper "UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement"
Learning-based underwater image enhancement (UIE) methods have made great progress. However, the lack of large-scale and high-quality paired training samples has become the main bottleneck hindering the development of UIE. The inter-frame information in underwater videos can accelerate or optimize the UIE process. Thus, we constructed the first large-scale high-resolution underwater video enhancement benchmark (UVEB) to promote the development of underwater vision. It contains 1,308 pairs of video sequences and more than 453,000 high-resolution with 38% Ultra-High-Definition (UHD) 4K frame pairs. UVEB comes from multiple countries, containing various scenes and video degradation types to adapt to diverse and complex underwater environments. We also propose the first supervised underwater video enhancement method, UVENet. UVE-Net converts the current frame information into convolutional kernels and passes them to adjacent frames for efficient inter-frame information exchange. By fully utilizing the redundant degraded information of underwater videos, UVE-Net completes video enhancement better. Experiments show the effective network design and good performance of UVE-Net.
update later
- Data Release.
- Code Release.
- 2024.02.27: Accepted by CVPR 2024!
- Part dataset is aviable at https://pan.baidu.com/s/14dl6K_KKi5of8aafhLqOXA extract code: 88di All dataset and code will be released later.