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Multi-scale Densely Connected Dehazing Network

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Intelligent Robotics and Applications (ICIRA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11743))

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Abstract

Single image dehazing is a challenging ill-posed problem. The traditional methods mainly focus on estimating the transmission of atmospheric-light medium with some priors or constraints. In this paper, we propose a novel end-to-end convolutional neural network (CNN) for image dehazing, called multi-scale densely connected dehazing network (MDCDN). The proposed network consists of a parallel multi-scale densely connected CNN network and an encoder-decoder U net. The parallel multi-scale dense-net can estimate transmission map accurately. The encoder-decoder U net is used to estimate the atmospheric light intensity. The all-in-one training can jointly learn the transmission map, atmospheric light, and dehazing images all together with jointly MSE error and a discriminator loss. We also create a dataset with indoor and outdoor data based on the LFSD, NLPR, and NYU2 depth datasets to train our network. Extensive experiments demonstrate that, in most cases, the proposed method achieves significant improvements over the state-of-the-art methods.

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Acknowledgements

The work presented in this paper was supported by the Natural Science Foundation of China under Grant No. 91648118.

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Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Tong Cui, Zhen Zhang, Yandong Tang & Jiandong Tian

  2. Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110016, China

    Tong Cui, Zhen Zhang, Yandong Tang & Jiandong Tian

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Tong Cui & Zhen Zhang

Authors
  1. Tong Cui
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  2. Zhen Zhang
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  3. Yandong Tang
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  4. Jiandong Tian
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Corresponding author

Correspondence to Yandong Tang .

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Editors and Affiliations

  1. Shenyang Institute of Automation, Shenyang, China

    Haibin Yu

  2. Shenyang Institute of Automation, Shenyang, China

    Jinguo Liu

  3. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  4. University of Portsmouth, Portsmouth, UK

    Zhaojie Ju

  5. Shenyang Institute of Automation, Shenyang, China

    Yuwang Liu

  6. University of Portsmouth, Portsmouth, UK

    Dalin Zhou

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Cui, T., Zhang, Z., Tang, Y., Tian, J. (2019). Multi-scale Densely Connected Dehazing Network. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11743. Springer, Cham. https://doi.org/10.1007/978-3-030-27538-9_51

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  • DOI: https://doi.org/10.1007/978-3-030-27538-9_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27537-2

  • Online ISBN: 978-3-030-27538-9

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