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.

翻译：水下视频增强（UVE）旨在提升水下视频的可见度与帧质量，对海洋研究与探索具有重要意义。然而，现有方法主要侧重于开发图像增强算法以独立处理每一帧，缺乏专门针对UVE任务的监督数据集与模型。为填补这一空白，我们构建了合成水下视频增强（SUVE）数据集，包含840段多样化水下风格视频及其对应的真实参考视频。基于该数据集，我们训练了新型水下视频增强模型UVENet，该模型利用帧间关系实现更优的增强性能。通过在合成与真实水下视频上的大量实验，我们验证了该方法的有效性。据我们所知，本研究首次对UVE进行了全面探索。代码公开于 https://anonymous.4open.science/r/UVENet。