In underwater scenes, the quality of the video and image acquired by the underwater imaging system suffers from severe degradation, influencing target detection and recognition. Thus, restoring real scenes from blurred videos and images is of great significance. Owing to the light absorption and scattering by suspended particles, the images acquired often have poor visibility, including color shift, low contrast, noise, and blurring issues. This paper aims to classify and compare some of the significant technologies in , presenting a comprehensive picture of the current research landscape for researchers. First we analyze the reasons for degradation of underwater images and the underwater optical imaging model. Then we classify the technologies into three categories, including image , image , and deep learning approaches. Afterward, we present the objective and analyze the state-of-the-art approaches. Finally, we summarize the shortcomings of the defogging approaches for underwater images and propose seven research directions.