The presence of fog degrades visibility in natural scene conditions. Computer vision applications like navigation, tracking, and surveillance need clear atmospheric images or videos as prerequisites for optimal performance. However, foggy atmosphere creates problems for computer vision applications due to reduced visibility. Different fog removal techniques are used to improve the visual quality of images and videos. The fog density depends on the depth information. Scene depth information estimation needs multiple images, which limits its real-life application. Hence, a single image fog removal requires some prior knowledge and/or assumptions to get the depth information. In this paper, the recent fog removal techniques are grouped into three broad categories: (1) filter-based methods, (2) color correction based methods, and (3) learning-based methods, for ease of understanding. The primary objective is to provide an introduction to this field and compare performance (both qualitative and quantitative) of representative techniques for each category. It is found that filter-based methods are doing overall better compared to other categories.

中文翻译：

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单幅图像去雾方法比较研究

雾的存在会降低自然场景条件下的能见度。导航、跟踪和监视等计算机视觉应用需要清晰的大气图像或视频作为获得最佳性能的先决条件。然而，由于能见度降低，雾气会给计算机视觉应用带来问题。使用不同的除雾技术来提高图像和视频的视觉质量。雾密度取决于深度信息。场景深度信息估计需要多幅图像，限制了其实际应用。因此，单个图像去雾需要一些先验知识和/或假设来获得深度信息。在本文中，最近的除雾技术分为三大类：（1）基于过滤器的方法，（2）基于颜色校正的方法，(3) 基于学习的方法，便于理解。主要目标是介绍该领域并比较每个类别的代表性技术的性能（定性和定量）。发现与其他类别相比，基于过滤器的方法总体上做得更好。