从朦胧到无朦胧:多图像、单图像和基于 CNN 的去雾算法综合概览

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jehoiada Jackson , Kwame Obour Agyekum , kwabena Sarpong , Chiagoziem Ukwuoma , Rutherford Patamia , Zhiguang Qin
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引用次数: 0

摘要

自然和人工散布的气候颗粒会改变在露天条件下获得的图像。由于能见度降低的气溶胶,雾、雾和霾等不利的气候条件会导致色彩变化,并降低所获图像的对比度。图像看起来会变形,对比度饱和度不足,对计算机视觉技术造成很大影响。去雾霾的目的是减少雾霾图像内部的不确定性,并增强后处理应用的视觉效果。然而,去雾霾因其数学上的模糊性而极具挑战性。本文回顾了过去十年间提出的主要图像去雾算法。论文介绍了图像降级的基础,然后将去雾算法分为基于增强的方法、基于联合的方法和图像修复方法。本文对所有技术进行了评估,并根据它们的属性介绍了相应的小节。接下来,我们使用结构相似性指数测量法(SSIM)、雾度结果、PSNR 和退化分数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,我们探讨了去雾霾方法的一些缺点和未来的研究范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hazy to hazy free: A comprehensive survey of multi-image, single-image, and CNN-based algorithms for dehazing

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.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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