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A Comprehensive Review of Computational Desmogging Techniques

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Abstract

The drivers’ visibility reduces significantly due to the presence of atmospheric pollutants such as mist, fog, smog, smoke, and haze. It causes failure of computer vision applications such as automated driving, remote monitoring, and object detection systems. There are various image desmogging techniques proposed in recent years that are essential for enhancing the performance of visibility restoration applications. As a result, image desmogging techniques incite the researchers. This article presents an overview and recent advances in visibility restoration using desmogging architectures. A general methodology of designing a desmogging model is presented. The existing desmogging techniques use an oblique gradient profile prior technique to estimate the transmission map. It describes the shape and sharpness of the edges in smoggy images. The estimated map is used to estimate atmospheric light. An exhaustive review of existing desmogging techniques, namely color enhancement techniques, filter-based techniques, prior-based techniques, variational model, and deep learning-based techniques is presented in terms of quality metrics, software, and hardware specifications. Furthermore, various performance metrics, namely structure similarity index, peak-signal-to-noise ratio, naturalness image quality evaluator, perception-based image quality evaluator, blind/referenceless image spatial quality evaluator, image entropy, and fog aware density evaluator are used to exploit the resultant images of existing techniques. The applications and significance of the existing desmogging algorithms are also presented. Eventually, the various challenges and future scope of desmogging techniques are discussed.

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Acknowledgements

This research is supported by Council of Scientific and Industrial Research (CSIR), India. The sanction number of the scheme is 22(0801)/19/EMR-II.

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  1. Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Sunil Kumar Singla

  2. Department of Information Technology, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar, India

    Vijay Kumar

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  1. Akshay Juneja
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Correspondence to Vijay Kumar.

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Juneja, A., Kumar, V. & Singla, S.K. A Comprehensive Review of Computational Desmogging Techniques. Arch Computat Methods Eng 30, 3723–3748 (2023). https://doi.org/10.1007/s11831-023-09918-9

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