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Physics-Based Feature Dehazing Networks

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12375))

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Abstract

We propose a physics-based feature dehazing network for image dehazing. In contrast to most existing end-to-end trainable network-based dehazing methods, we explicitly consider the physics model of the haze process in the network design and remove haze in a deep feature space. We propose an effective feature dehazing unit (FDU), which is applied to the deep feature space to explore useful features for image dehazing based on the physics model. The FDU is embedded into an encoder and decoder architecture with residual learning, so that the proposed network can be trained in an end-to-end fashion and effectively help haze removal. The encoder and decoder modules are adopted for feature extraction and clear image reconstruction, respectively. The residual learning is applied to increase the accuracy and ease the training of deep neural networks. We analyze the effectiveness of the proposed network and demonstrate that it can effectively dehaze images with favorable performance against state-of-the-art methods.

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Author information

Authors and Affiliations

  1. Max Planck Institute for Informatics, Saarbrucken, Germany

    Jiangxin Dong

  2. Nanjing University of Science and Technology, Nanjing, China

    Jinshan Pan

Authors
  1. Jiangxin Dong
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  2. Jinshan Pan
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Correspondence to Jinshan Pan .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Dong, J., Pan, J. (2020). Physics-Based Feature Dehazing Networks. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12375. Springer, Cham. https://doi.org/10.1007/978-3-030-58577-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-58577-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58576-1

  • Online ISBN: 978-3-030-58577-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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