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 underwater image defogging, 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 underwater image defogging technologies into three categories, including image restoration approaches, image enhancement approaches, and deep learning approaches. Afterward, we present the objective evaluation metrics 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.

在水下场景中，由水下成像系统获取的视频和图像的质量会严重下降，从而影响目标的检测和识别。因此，从模糊的视频和图像中恢复真实场景具有重要意义。由于悬浮颗粒对光的吸收和散射，所获取的图像通常具有较差的可见性，包括色偏，低对比度，噪点和模糊问题。本文旨在对水下图像除雾中的一些重要技术进行分类和比较，为研究人员提供当前研究前景的全面图片。首先，我们分析了水下图像退化的原因和水下光学成像模型。然后，我们将水下图像除雾技术分为三类：包括图像恢复方法，图像增强方法和深度学习方法。随后，我们提出了客观的评估指标并分析了最新方法。最后，我们总结了水下图像去雾方法的缺点，并提出了七个研究方向。