RUE-Net: Advancing Underwater Vision with Live Image Enhancement

The task of underwater image enhancement aims to improve the quality of the images and promote the visualization effect. Current methods still face some challenges, which include the difficulty to model the real ocean environment and the lack of simultaneous exploitation and modeling of both global and local information in images. Particularly, in resource-constrained underwater scenarios, models need to enhance images on embedded devices in real-time, which usually requires them to strike a balance between the inference speed and accuracy. In this paper, we put forward a real-time underwater image enhancement network (RUE-Net). Compared to previous advanced models using single branch structure or traditional U-shaped networks, it consists of parallel Receptive Field Enhancement (RFE) Module and Fine Grain Detail (FGD) Module, which perform parallel modeling and fusion of image information at global and local scales. Furthermore, dense connections are established in each stage throughout the whole model. Compared with current models, RUE-Net shows the best performance in the image enhancement task in LSUI, UFO-120, and EUVP datasets. Furthermore, based on the RUE-Net, we introduce a simultaneous enhancement and super-resolution (SESR) multi-task model. The experiments prove RUE-Net has an excellent capability for image restoration and super-resolution reconstruction in UFO-120 and USR-248 datasets. RUE-Net is also deployed on the Jetson Orin Nano, which achieves an impressive inference speed of up to 51 frames per second (FPS), demonstrating its value in underwater robot with limited computing capabilities. The code is available at https://github.com/GuocunWang/RUE-Net.