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Review of dehazing techniques: challenges and future trends

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Abstract

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear map** between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empirical insights, the study meticulously elucidates simulation results and presents an in-depth analysis of prominent dehazing techniques. By orchestrating a seamless integration of theoretical exposition and practical evaluation, this research contributes to the advancement of the field, paving the way for improved image and video fidelity in the presence of atmospheric haze.

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Acknowledgements

The authors are very grateful to all institutions in the affiliation list for successfully performing this research work. The authors would like to thank Prince Sultan University for their support.

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Authors and Affiliations

  1. Department of Electronics and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Abeer Ayoub, Walid El-Shafai, Fathi E. Abd El-Samie & El-Sayed M. EL-Rabaie

  2. Security Engineering Lab, Computer Science Department, Prince Sultan University, 11586, Riyadh, Saudi Arabia

    Walid El-Shafai

  3. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

  4. Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, 81542, Egypt

    Ehab K. I. Hamad

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  1. Abeer Ayoub
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Ayoub, A., El-Shafai, W., El-Samie, F.E.A. et al. Review of dehazing techniques: challenges and future trends. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17603-z

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