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Underwater image enhancement (UIE) poses challenges due to distinctiveproperties of the underwater environment, including low contrast, highturbidity, visual blurriness, and color distortion. In recent years, theapplication of deep learning has quietly revolutionized various areas ofscientific research, including UIE. However, existing deep learning-based UIEmethods generally suffer from issues of weak robustness and limitedadaptability. In this paper, inspired by residual and attention mechanisms, wepropose a more reliable and reasonable UIE network called RAUNE-Net byemploying residual learning of high-level features at the network's bottle-neckand two aspects of attention manipulations in the down-sampling procedure.Furthermore, we collect and create two datasets specifically designed forevaluating UIE methods, which contains different types of underwaterdistortions and degradations. The experimental validation demonstrates that ourmethod obtains promising objective performance and consistent visual resultsacross various real-world underwater images compared to other eight UIEmethods. Our example code and datasets are publicly available athttps://github.com/fansuregrin/RAUNE-Net.

RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method