在恶劣天气条件下（阴霾、雾、烟雾、薄雾等）获取的图像通常会严重退化。大气中存在两种颗粒：干颗粒（灰尘、烟雾等）和湿颗粒（水滴、雨等）。由于这些颗粒的散射和吸收，会产生各种不利影响，包括能见度降低以及对比度、颜色失真等。 图片中介绍了。这些退化的图像对于许多计算机视觉应用来说是不可接受的，例如智能交通、视频监控、天气预报、遥感等。 与减轻这种影响相关的计算机视觉任务称为图像去雾。需要高质量的输入图像（无雾）来确保这些应用程序的准确工作，由图像去雾方法提供。拍摄图像中的雾霾效果取决于观察者到场景的距离。此外，粒子的散射给捕获的图像增加了非线性和数据相关的噪声。单幅图像去雾利用模糊图像形成的物理模型，其中深度或透射率的估计是获得无雾图像的重要参数。这篇评论文章将最近的去雾方法分为不同的类别，并详细阐述了每个类别的流行去雾方法。这种对不同去雾方法的分类分析表明，近年来，深度学习和基于恢复的先验方法在解决图像去雾的两个具有挑战性的问题方面引起了研究人员的注意：密集雾度和非均匀雾度。此外，最近，引入了基于硬件实现的方法来辅助智能交通系统。本文提供了该领域的深入知识；迄今为止取得的进展并比较最新作品的性能（定性和定量）。它从新的角度详细描述了去雾方法、动机、用于测试的流行和具有挑战性的数据集、用于评估的指标以及该领域的问题/挑战。本文将对从新手到该领域经验丰富的所有类型的研究人员都有用。它还表明了该领域缺乏最新方法的研究空白。

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A Comprehensive Review on Analysis and Implementation of Recent Image Dehazing Methods

Images acquired in poor weather conditions (haze, fog, smog, mist, etc.) are often severely degraded. In the atmosphere, there exists two types of particles: dry particles (dust, smoke, etc.) and wet particles (water droplets, rain, etc.) Due to the scattering and absorption of these particles, various adverse effects, including reduced visibility and contrast, color distortions, etc. are introduced in the image. These degraded images are not acceptable for many computer vision applications such as smart transportation, video surveillance, weather forecasting, remote sensing, etc. The computer vision task associated with the mitigation of this effect is known as image dehazing. A high-quality input image (haze-free) is required to ensure the accurate working of these applications, supplied by image dehazing methods. The haze effect in the captured image is dependent on the distance from the observer to the scene. Besides, the scattering of particles adds non-linear and data-dependent noise to the captured image. Single image dehazing utilizes the physical model of hazy image formation in which estimation of depth or transmission is an important parameter to obtain a haze-free image. This review article groups the recent dehazing methods into different categories and elaborates the popular dehazing methods of each category. This category-wise analysis of different dehazing methods reveals that the deep learning and the restoration-based methods with priors have attracted the attention of the researchers in recent years in solving two challenging problems of image dehazing: dense haze and non-homogeneous haze. Also, recently, hardware implementation-based methods are introduced to assist smart transportation systems. This paper provides in-depth knowledge of this field; progress made to date and compares performance (both qualitative and quantitative) of the latest works. It covers a detailed description of dehazing methods, motivation, popular, and challenging datasets used for testing, metrics used for evaluation, and issues/challenges in this field from a new perspective. This paper will be useful to all types of researchers from novice to highly experienced in this field. It also suggests research gaps in this field where recent methods are lacking.