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)
Currently, optical imaging 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 backscattering noise and improve the signal-to-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. Images obtained in a dark underwater scene usually include more noise and have very low visual quality, which may easily lead to artifacts during the process of enhancement. To bridge this gap, we thoroughly 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. Due to the lack of ready-made datasets for training, we also propose the first dataset for low-light underwater image enhancement to train our model. Our method can be implemented to skillfully and simultaneously address low-light degradation and scattering degradation in low-light underwater images. Experimental results also show that our method is robust against different illumination levels, which greatly expands the applicable scenarios of our method. Compared with previous underwater image enhancement methods and low-light image enhancement methods, outstanding performance is achieved using our method in various low-light underwater scenes.