Underwater images are affected by factors such as scattering and absorption in water, resulting in severe image quality degradation. Scene depth, as an important parameter in images, plays a key role in underwater image restoration. It can be used as an intermediate parameter in physical model-based restoration and as a feature in deep learning. Firstly, this study introduces an underwater imaging model based on the principle of underwater image restoration. Secondly, it focuses on analyzing the application of physical models and deep learning in scene depth estimation and image restoration. By classifying and summarizing these different methods, the study compares their advantages and disadvantages to reveal the core role of scene depth in degradation modeling and restoration optimization. Thirdly, several algorithms are compared through experiments from both subjective and objective aspects to analyze their advantages and limitations. Finally, an outlook is presented to provide new ideas and directions for the future development of underwater image restoration.