Abstract: With the vigorous development of image processing technology and computer vision technology, scene detection and image processing under special weather have become an important research direction in this field. And images taken in a foggy day are easily affected by fog or haze, resulting in blurry details and low contrast, so that important image information is lost. In order to solve such problems, image defogging algorithms have came into being. The image defogging algorithm is an image analysis and processing method for the purpose of meeting the needs of specific scenes, highlighting the details of the picture, and enhancing the quality of the picture. In order to study the development process, current situation and future of image defogging algorithms, in this paper, according to different principles, the defogging algorithms are divided into three categories：defogging algorithms based on physical models, defogging algorithms based on non-physical models, and defogging algorithms based on deep learning. Then it introduces the content, development, advantages and disadvantages of the classic algorithms among them, and conducts experimental analysis and comparison on them. At the end of the article, the important and difficult points of the future research of the dehazing algorithm are prospected.

Key words: image defogging, image processing, physical model, non-physical model, deep learning

摘要： 随着图像处理技术和计算机视觉技术的蓬勃发展，对特殊天气下的场景检测和图像处理成为该领域的重要研究方向。其中在雾天拍摄的图像容易受雾或霾的影响，导致图片细节模糊、对比度低以至于丢失图像重要信息，为解决此类问题图像去雾算法应运而生。图像去雾算法是以满足特定场景需求、突出图片细节并增强图片质量为目的的一种图像分析与处理方法。为了研究图像去雾算法的发展过程、现状以及未来，根据原理不同将去雾算法分为基于物理模型去雾算法、基于非物理模型去雾算法和基于深度学习去雾算法三大类。对其中经典算法从内容、发展和优缺点等方面进行介绍；并对算法进行实验分析与比较。展望了去雾算法的未来研究的重难点。

关键词: 图像去雾, 图像处理, 物理模型, 非物理模型, 深度学习