Single Image Defogging using Deep Learning Techniques: Past, Present and Future

Archives of Computational Methods in Engineering (2021)Cite this article

Abstract

Image dehazing play a vital role in several applications related to computer vision. The prime motive of this paper is to provide the overview of the existing deep learning algorithms associated with image defogging. In beginning, the main issues preset in the existing single image technique based on physical models are discussed. Thereafter, the basic concept of atmospheric scattering model and deep learning are discussed. The existing deep learning approaches based on single image defogging are decomposed into 3 broad categories namely Convolution neural network, Recurrent neural network, and Generative adversarial network with their pro and cons are discussed. The synthesised and real datasets used in defogging techniques are discussed with their applications. It also describes the various challenges and issues in the existing image dehazing techniques.

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Acknowledgements

This work is funded by the Council of Scientific and Industrial Research (CSIR), India. This research is under grant with File No. 22(0801)/19/EMR-II.

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

    Neeraj Sharma & Sunil Kumar Singla

  2. Department of Computer Science and Engineering, NIT Hamirpur, Hamirpur, India

    Vijay Kumar

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  1. Neeraj Sharma
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  2. Vijay Kumar
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  3. Sunil Kumar Singla
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Correspondence to Neeraj Sharma.

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Sharma, N., Kumar, V. & Singla, S.K. Single Image Defogging using Deep Learning Techniques: Past, Present and Future. Arch Computat Methods Eng (2021). https://doi.org/10.1007/s11831-021-09541-6

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