2023/11/01 by Wangzhen Peng, Peng, Wangzhen, Chenghao Zhou +7
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image Enhancement Techniques #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.00246
openalex publication_date 2023/11/01 · openalex created_date 2023/11/03 · openalex updated_date 2026/07/28
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.