  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 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.
