Lighting the darkness in the sea: A deep learning model for underwater image enhancement - Xie, Yaofeng; Yu, Zhibin; Yu, Xiao; Zheng, Bing (Frontiers in Marine Science, 2022)
Nowadays, optical imaging-based cameras are widely used on underwater vehicles to obtain images and support numerous marine exploration tasks. Many underwater image enhancement algorithms have been proposed in the past few years to suppress the underwater backscattering noise and improve the signal-noise ratio of underwater images. However, these algorithms are mainly focused on underwater image enhancement tasks in a bright environment. Thus, it is still unclear how these algorithms would perform on images acquired in an underwater scene with low illumination. Actually, images obtained in a dark underwater scene usually include more noise and very low visual quality which may easily trigger artifacts in the process of enhancement. To bridge this gap, we deeply study the existing underwater image enhancement methods and low illumination image enhancement methods based on deep learning and propose a new underwater image enhancement network to solve the problem of serious degradation of underwater image quality in a low illumination environment. Our method can brighten the dark area of the input image and display the invisible objects in the dark area, which cannot be achieved by conventional underwater image enhancement methods. At the same time, our method can also enhance underwater images with different illumination and a large number of experimental results show that our method can greatly improve the quality of underwater images under low light levels.