RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method
This work addresses underwater image enhancement for applications like marine research or robotics, but it appears incremental as it builds on existing deep learning approaches with specific architectural tweaks.
The authors tackled the problem of underwater image enhancement by proposing RAUNE-Net, a method that uses residual and attention mechanisms to improve robustness and adaptability, achieving promising objective performance and consistent visual results compared to eight other methods.
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.