关键词: 深度学习, 图像预处理, 物理模型, 水下图像

Abstract:

Underwater image is an important carrier of ocean information. Due to the complexity of underwater environment, the original underwater image is affected by a lot of noise, which affects the subsequent detection tasks. Therefore, underwater image preprocessing has become a hot research topic. In order to analyze the current situation and trend of the research on the underwater image preprocessing driven by deep learning, this paper summarizes and analyzes the relevant literature at home and abroad in recent years. Firstly, the paper introduces two kinds of traditional underwater image preprocessing methods, and analyzes their advantages and disadvantages. Secondly, the underwater image preprocessing method driven by deep learning is analyzed according to whether combined with the physical model, then it compares and summarizes the related methods. Thirdly, the improvement of deep learning method is analyzed, mainly from two aspects of lightweight and improving robustness and adaptability. Finally, the existing problems of deep learning driven underwater image preprocessing are discussed, and the development direction of research is prospected.

Key words: deep learning, image preprocessing, physical model, underwater image