Underwater robots have broad applications in many fields such as ocean exploration, ocean pasture and environmental monitoring. However, due to the inference of light scattering and absorption, selective color attenuation, suspended particles and other complex factors in the underwater environment, it is difficult for robot vision sensors to obtain high-quality underwater image signal, which is the bottleneck problem that restricts the visual perception of underwater robots. In this paper, we propose a multi-scale fusion generative adversarial network named Fusion Water-GAN (FW-GAN) to enhance the underwater image quality. The proposed model has four convolution branches, these branches refine the features of the three prior inputs and encode the original input, then fuse prior features using the proposed multi-scale fusion connections, and finally use the channel attention decoder to generate satisfactory enhanced results. We conduct qualitative and quantitative comparison experiments on real-world and synthetic distorted underwater image datasets under various degradation conditions. The results show that compared with the recent state-of-the-art underwater image enhancement methods, our proposed method achieves higher quantitative metrics scores and better generalization capability. In addition, the ablation study demonstrated the contribution of each component.

水下机器人在海洋勘探、海洋牧场、环境监测等诸多领域有着广泛的应用。然而，由于水下环境中光的散射和吸收、选择性颜色衰减、悬浮颗粒等复杂因素的影响，机器人视觉传感器难以获得高质量的水下图像信号，这是制约​​机器人视觉传感器获取高质量水下图像信号的瓶颈问题。水下机器人的视觉感知。在本文中，我们提出了一种名为 Fusion Water-GAN (FW-GAN) 的多尺度融合生成对抗网络来提高水下图像质量。提出的模型有四个卷积分支，这些分支细化三个先验输入的特征并对原始输入进行编码，然后使用提出的多尺度融合连接融合先验特征，最后使用通道注意力解码器产生令人满意的增强结果。我们在各种退化条件下对真实世界和合成的失真水下图像数据集进行定性和定量比较实验。结果表明，与最近最先进的水下图像增强方法相比，我们提出的方法实现了更高的量化指标得分和更好的泛化能力。此外，消融研究证明了每个组件的贡献。结果表明，与最近最先进的水下图像增强方法相比，我们提出的方法实现了更高的量化指标得分和更好的泛化能力。此外，消融研究证明了每个组件的贡献。结果表明，与最近最先进的水下图像增强方法相比，我们提出的方法实现了更高的量化指标得分和更好的泛化能力。此外，消融研究证明了每个组件的贡献。