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.

提出了一种用于实时增强水下图像的新型 U 形网络 LU2Net，通过引入轴向深度卷积和通道注意力模块，显著降低了计算需求和模型参数，从而提高了处理速度，并能以比现有最先进的水下图像增强方法快 8 倍的速度提供增强后的水下图像。此外，LU2Net 还能够处理实时水下视频增强。