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Photo-realistic dehazing via contextual generative adversarial networks

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Abstract

Single image dehazing is a challenging task due to its ambiguous nature. In this paper we present a new model based on generative adversarial networks (GANs) for single image dehazing, called as dehazing GAN. In contrast to estimating the transmission map and the atmospheric light separately as most existing deep learning methods, dehazing GAN restores the corresponding hazy-free image directly from a hazy image via a generative adversarial network. Extensive experimental results on both synthetic dataset and real-world images show our model outperforms the state-of-the-art algorithms.

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Acknowledgements

This work is supported by National Key R&D Program of China (Grant No. 2017YFB0503004), National Natural Science Foundation of China (Grant No. 41571436) and China Postdoctoral Science Foundation (Grant No. 2019M662709).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Shengdong Zhang & Fazhi He

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Wenqi Ren

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  1. Shengdong Zhang
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  2. Fazhi He
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Correspondence to Fazhi He.

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Zhang, S., He, F. & Ren, W. Photo-realistic dehazing via contextual generative adversarial networks. Machine Vision and Applications 31, 33 (2020). https://doi.org/10.1007/s00138-020-01082-5

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