Abstract
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.
Abstract (translated)
水下视频增强（UVE）旨在提高水下视频的可见度和帧质量，这对海洋研究和探索具有重要的影响。然而，现有的方法主要关注开发用于独立增强每个帧的图像增强算法。目前缺乏针对UVE任务的监督数据和模型。为了填补这一空白，我们构建了合成水下视频增强（SUVE）数据集，包括840个不同水下风格的视频与地面参考视频的配对。基于这个数据集，我们训练了一种新颖的水下视频增强模型——UVENet，它利用跨帧关系实现更好的增强性能。通过对合成和真实水下视频的广泛实验，我们证明了我们的方法的有效性。本研究是我们知识范围内对UVE的首次全面探索。代码可于https://anonymous.4open.science/r/UVENet获取。
URL
https://arxiv.org/abs/2403.11506
