LU2Net: A Lightweight Network for Real-time Underwater Image Enhancement
Haodong Yang, Jisheng Xu, Zhiliang Lin, Jianping He

TL;DR
LU2Net is a lightweight, real-time underwater image enhancement network that significantly improves processing speed and quality, enabling effective underwater vision for robots.
Contribution
The paper introduces LU2Net, a novel efficient U-shaped network with axial depthwise convolution and channel attention for real-time underwater image enhancement.
Findings
8 times faster than state-of-the-art methods
Effective enhancement of underwater images in real-time
Capable of real-time underwater video enhancement
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Underwater Vehicles and Communication Systems · Image and Signal Denoising Methods
MethodsSoftmax · Attention Is All You Need · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Depthwise Convolution · Convolution
