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DHSGAN: An End to End Dehazing Network for Fog and Smoke

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Computer Vision – ACCV 2018 (ACCV 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11365))

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Abstract

In this paper we propose a novel end-to-end convolution dehazing architecture, called De-Haze and Smoke GAN (DHSGAN). The model is trained under a generative adversarial network framework to effectively learn the underlying distribution of clean images for the generation of realistic haze-free images. We train the model on a dataset that is synthesized to include image degradation scenarios from varied conditions of fog, haze, and smoke in both indoor and outdoor settings. Experimental results on both synthetic and natural degraded images demonstrate that our method shows significant robustness over different haze conditions in comparison to the state-of-the-art methods. A group of studies are conducted to evaluate the effectiveness of each module of the proposed method.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology Kanpur, Kanpur, India

    Ramavtar Malav & Soumya Ranjan Sahoo

  2. Korea Advanced Institute of Science and Technology, Daejeon, South Korea

    Ayoung Kim

  3. Ford Motor Company, Palo Alto, CA, USA

    Gaurav Pandey

Authors
  1. Ramavtar Malav
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  2. Ayoung Kim
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  3. Soumya Ranjan Sahoo
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  4. Gaurav Pandey
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Corresponding author

Correspondence to Ramavtar Malav .

Editor information

Editors and Affiliations

  1. IIIT Hyderabad, Hyderabad, India

    C.V. Jawahar

  2. ANU, Canberra, ACT, Australia

    Hongdong Li

  3. Simon Fraser University, Burnaby, BC, Canada

    Greg Mori

  4. ETH Zurich, Zurich, Zürich, Switzerland

    Konrad Schindler

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Malav, R., Kim, A., Sahoo, S.R., Pandey, G. (2019). DHSGAN: An End to End Dehazing Network for Fog and Smoke. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11365. Springer, Cham. https://doi.org/10.1007/978-3-030-20873-8_38

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  • DOI: https://doi.org/10.1007/978-3-030-20873-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20872-1

  • Online ISBN: 978-3-030-20873-8

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