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A Comprehensive Review of Computational Dehazing Techniques

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Abstract

The visibility of outdoor images is greatly degraded due to the presence of fog, haze, smog etc. The poor visibility may cause the failure of computer vision applications such as intelligent transportation systems, surveillance systems, object tracking systems, etc. To resolve this problem, many image dehazing techniques have been developed. These techniques play an important role in improving performance of various computer vision applications. Due to this, the researchers are attracted toward the dehazing techniques. This paper carries out a comprehensive review of dehazing techniques to show that these could be effectively applied in real-life practice. On the other hand, it encourages the researchers to use these techniques for removal of haze from hazy images. The seven main classes of dehazing technique, such as depth estimation, wavelet, enhancement, filtering, supervised learning, fusion, meta-heuristic techniques and variational model are addressed. In addition, this paper focuses on mathematical models of dehazing techniques along with their implementation aspects. Finally, some considerations about challenges and future scope in dehazing techniques are discussed.

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  1. Thapar institute of engineering and technology, Patiala, Punjab, 147001, India

    Dilbag Singh & Vijay Kumar

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Singh, D., Kumar, V. A Comprehensive Review of Computational Dehazing Techniques. Arch Computat Methods Eng 26, 1395–1413 (2019). https://doi.org/10.1007/s11831-018-9294-z

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