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HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12351))

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Abstract

In this paper, we present a Haze-Aware Representation Distillation Generative Adversarial Network (HardGAN) for single-image dehazing. Unlike previous studies that intend to model the transmission map and global atmospheric light jointly to restore a clear image, we approach this restoration problem by using a multi-scale structure neural network composed of our proposed haze-aware representation distillation layers. Moreover, we re-introduce to utilize the normalization layer skillfully instead of stacking with the convolutional layers directly as before to avoid useful information wash away, as claimed in many image quality enhancement studies. Extensive experiments on several synthetic benchmark datasets as well as the NTIRE 2020 real-world images show our proposed HardGAN performs favorably against the state-of-the-art methods in terms of PSNR, SSIM, LPIPS, and individual subjective evaluation.

Q. Deng and Z. Huang are equal contribution.

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Notes

  1. 1.

    The authors from Taiwan universities and ByteDance completed the experiments.

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Acknowledgments

This work was funded in part by Qualcomm through a Taiwan University Research Collaboration Project and in part by the Ministry of Science and Technology, Taiwan, under grant MOST 109-2634-F-007-013.

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Authors and Affiliations

  1. National Tsing Hua University, Hsinchu, 30013, Taiwan

    Ziling Huang & Chia-Wen Lin

  2. ByteDance AI Lab, Beijing, China

    Qili Deng

  3. Qualcomm Technologies, Inc., San Diego, USA

    Chung-Chi Tsai

Authors
  1. Qili Deng
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  2. Ziling Huang
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  3. Chung-Chi Tsai
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  4. Chia-Wen Lin
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Corresponding author

Correspondence to Ziling Huang .

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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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Deng, Q., Huang, Z., Tsai, CC., Lin, CW. (2020). HardGAN: A Haze-Aware Representation Distillation GAN for Single Image Dehazing. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12351. Springer, Cham. https://doi.org/10.1007/978-3-030-58539-6_43

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  • DOI: https://doi.org/10.1007/978-3-030-58539-6_43

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