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 this http URL 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.
Abstract (translated)
基于学习的 underwater图像增强（UIE）方法取得了很大的进展。然而，缺乏大规模和高质量的成对训练样本已成为阻碍UIE发展的主要瓶颈。水下视频中的帧间信息可以加速或优化UIE过程。因此，我们构建了第一个大规模高分辨率水下视频增强基准（UVEB）以促进水下图像增强的发展。 UVEB来自多个国家，包含各种场景和视频衰退类型，以适应多样和复杂的水下环境。我们还提出了第一个监督式水下视频增强方法，UVE-Net。UVE-Net将当前帧信息转换为卷积内核并传递给相邻帧进行有效的帧间信息交流。通过充分利用水下视频的冗余衰退信息，UVE-Net完成视频增强效果更好。实验结果表明，UVE-Net的有效的网络设计和良好的性能。
URL
https://arxiv.org/abs/2404.14542
