UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
Yaofeng Xie, Lingwei Kong, Kai Chen, Ziqiang Zheng, Xiao Yu, Zhibin Yu, and Bing Zheng

TL;DR
This paper introduces UVEB, a large-scale underwater video enhancement benchmark, and proposes UVE-Net, a novel supervised method leveraging inter-frame information for improved underwater video quality.
Contribution
The paper provides the first large-scale high-resolution underwater video benchmark and a new supervised enhancement method utilizing inter-frame information exchange.
Findings
UVEB contains 1,308 paired video sequences with over 453,000 high-resolution frames.
UVE-Net effectively utilizes inter-frame information for superior enhancement performance.
Experiments demonstrate the effectiveness of UVE-Net and the utility of the UVEB benchmark.
Abstract
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, UVE-Net. UVE-Net converts the current frame information into convolutional…
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Taxonomy
TopicsUnderwater Vehicles and Communication Systems · Image Enhancement Techniques · Underwater Acoustics Research
