The natural and artificial dispersal of climatic particles transforms images obtained in open-air conditions. Due to visibility diminishing aerosols, unfavorable climate situations such as mist, fog, and haze cause color change and reduce the contrast of the obtained image. Images seem deformed and inadequate in contrast saturation, affecting computer vision techniques considerably. Haze removal aims to decrease uncertainty inside a hazy image and enhance the visual effects for post-processing applications. However, dehazing is highly challenging due to its mathematical obscurity. This paper reviews the primary algorithms for image dehazing proposed over the past decade. The paper presents the basis for hazy image degradation, followed by a novel classification of dehazing algorithms into enhancement-based, joint-based, and Image repair methods. All techniques are evaluated, and the respective subsections are presented according to their attributes. Next, we categorize and elaborate on the various quality assessment methods using structural similarity index measure(SSIM), haze result, PSNR, and degradation score to evaluate some unique algorithms. Ultimately, some concerns about drawbacks and future research scope in haze removal methods are examined.

中文翻译：

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从朦胧到无朦胧：多图像、单图像和基于 CNN 的去雾算法的全面调查


气候颗粒的自然和人工扩散改变了在露天条件下获得的图像。由于能见度降低气溶胶，薄雾、雾和霾等不利气候情况会导致颜色变化并降低所获图像的对比度。图像似乎变形且对比度饱和度不足，严重影响计算机视觉技术。除霾旨在减少朦胧图像中的不确定性，并增强后处理应用的视觉效果。然而，由于其数学上的晦涩难懂性，去雾极具挑战性。本文回顾了过去十年中提出的图像去雾的主要算法。本文提出了朦胧图像退化的基础，然后将去雾算法新分类为基于增强、基于关节和图像修复的方法。评估所有技术，并根据其属性呈现相应的小节。接下来，我们使用结构相似性指数测量 （SSIM） 、雾度结果、PSNR 和退化评分对各种质量评估方法进行分类和阐述，以评估一些独特的算法。最后，研究了对除雾方法的缺点和未来研究范围的一些担忧。