End-To-End Underwater Video Enhancement: Dataset and Model
Dazhao Du Enhan Li Lingyu Si Fanjiang Xu Jianwei Niu
Underwater video enhancement (UVE) aims to improve the visibility and frame
quality of underwater videos, which has significant implications for marine
research and exploration. However, existing methods primarily focus on
developing image enhancement algorithms to enhance each frame independently.
There is a lack of supervised datasets and models specifically tailored for UVE
tasks. To fill this gap, we construct the Synthetic Underwater Video
Enhancement (SUVE) dataset, comprising 840 diverse underwater-style videos
paired with ground-truth reference videos. Based on this dataset, we train a
novel underwater video enhancement model, UVENet, which utilizes inter-frame
relationships to achieve better enhancement performance. Through extensive
experiments on both synthetic and real underwater videos, we demonstrate the
effectiveness of our approach. This study represents the first comprehensive
exploration of UVE to our knowledge. The code is available at
https://anonymous.4open.science/r/UVENet.