RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement
  Method
Wangzhen Peng Chenghao Zhou Runze Hu Jingchao Cao Yutao Liu
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.