A Review of Underwater Image Enhancement and Restoration Techniques Based on Gan

In recent years, underwater image enhancement and restoration technology have significantly contributed to the efficiency of marine operations and the advancement of seabed resource development, which carries substantial academic significance and practical value. This study initially investigates and assesses the underwater imaging model based on the principles of underwater imaging, emphasizing the challenges and issues faced by current technology. Secondly, it thoroughly presents the pertinent research on underwater image enhancement and restoration technologies utilizing generative adversarial networks, and provides an in-depth classification and analysis of the prevailing underwater image enhancement and restoration techniques based on GAN. The study of experimental findings highlights the peculiarities of various classification methods. Thirdly, it summarizes the commonly used datasets and evaluation indicators. Finally, it forecasts and examines the viable development pathways for underwater image enhancement and restoration technology moving forward, particularly emphasizing the significant potential and application value of generative adversarial networks within this domain.