Computer vision techniques have empowered underwater robots to effectively undertake a multitude of tasks, including object tracking and path planning. However, underwater optical factors like light refraction and absorption present challenges to underwater vision, which cause degradation of underwater images. A variety of underwater image enhancement methods have been proposed to improve the effectiveness of underwater vision perception. Nevertheless, for real-time vision tasks on underwater robots, it is necessary to overcome the challenges associated with algorithmic efficiency and real-time capabilities. In this paper, we introduce Lightweight Underwater Unet (LU2Net), a novel U-shape network designed specifically for real-time enhancement of underwater images. The proposed model incorporates axial depthwise convolution and the channel attention module, enabling it to significantly reduce computational demands and model parameters, thereby improving processing speed. The extensive experiments conducted on the dataset and real-world underwater robots demonstrate the exceptional performance and speed of proposed model. It is capable of providing well-enhanced underwater images at a speed 8 times faster than the current state-of-the-art underwater image enhancement method. Moreover, LU2Net is able to handle real-time underwater video enhancement.

翻译：计算机视觉技术使水下机器人能够有效执行多种任务，包括目标跟踪与路径规划。然而，水下光学因素（如光线折射与吸收）对水下视觉构成挑战，导致水下图像质量退化。为提升水下视觉感知效果，已有多种水下图像增强方法被提出。然而，对于水下机器人的实时视觉任务，必须克服算法效率与实时性方面的挑战。本文提出轻量级水下Unet网络（LU2Net），这是一种专为实时水下图像增强设计的新型U型网络。该模型融合了轴向深度卷积与通道注意力模块，使其能显著降低计算需求与模型参数量，从而提升处理速度。在数据集及真实水下机器人上进行的大量实验表明，所提模型具有卓越的性能与速度。其能以比当前最先进水下图像增强方法快8倍的速度提供高质量增强的水下图像。此外，LU2Net能够处理实时水下视频增强任务。