Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion. In recent years, the application of deep learning has quietly revolutionized various areas of scientific research, including UIE. However, existing deep learning-based UIE methods generally suffer from issues of weak robustness and limited adaptability. In this paper, inspired by residual and attention mechanisms, we propose a more reliable and reasonable UIE network called RAUNE-Net by employing residual learning of high-level features at the network's bottle-neck and two aspects of attention manipulations in the down-sampling procedure. Furthermore, we collect and create two datasets specifically designed for evaluating UIE methods, which contains different types of underwater distortions and degradations. The experimental validation demonstrates that our method obtains promising objective performance and consistent visual results across various real-world underwater images compared to other eight UIE methods. Our example code and datasets are publicly available at https://github.com/fansuregrin/RAUNE-Net.

翻译：水下图像增强（UIE）因水下环境的独特特性（包括低对比度、高浊度、视觉模糊和色彩失真）而面临诸多挑战。近年来，深度学习技术的应用悄然革新了包括UIE在内的诸多科学研究领域。然而，现有基于深度学习的UIE方法普遍存在鲁棒性弱和适应性有限的问题。本文受残差机制和注意力机制启发，通过在网络瓶颈处采用高层特征的残差学习以及下采样过程中的双重注意力操作，提出了一种更可靠且更合理的UIE网络RAUNE-Net。此外，我们收集并构建了两个专门用于评估UIE方法的数据集，其中包含不同类型的水下失真与退化。实验验证表明，与其它八种UIE方法相比，我们的方法在多种真实水下图像上取得了令人满意的客观性能与一致的视觉效果。我们的示例代码和数据集已在https://github.com/fansuregrin/RAUNE-Net公开。