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Residual Learning Dehazing Net

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Book cover Advances in Multimedia Information Processing – PCM 2018 (PCM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11164))

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Abstract

Single haze removal is a challenging ill-posed problem. Most existing methods solving this dilemma depend on atmospheric physical scattering model. In other words, they recover haze-free images by estimating the atmospheric transmission. In this paper, we proposed a new recovery model called Residual Adding model, which takes dehazing procedure as a hazy image adding a loss image. Based on this new model, we proposed a single image dehazing network built with Conditional Generative Adversarial Nets (CGAN), called Residual Learning Dehazing Network (RLD-Net). Benefiting from the new model, the RLD-Net is designed as not only an end-to-end dehazing network but also a point-to-point mapping network. That means RLD-Net can take a hazy image as input and a corresponding clear image as output without any extract calculation like inversing atmospheric physical scattering model. Experimental results on both synthesized hazy images and real-world hazy images demonstrate our outstanding performance.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Yili Gu & Xinguang Xiang

Authors
  1. Yili Gu
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  2. Xinguang Xiang
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Corresponding author

Correspondence to Xinguang Xiang .

Editor information

Editors and Affiliations

  1. Hefei University of Technology, Hefei, China

    Richang Hong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. University of Tokyo, Tokyo, Japan

    Toshihiko Yamasaki

  4. Hefei University of Technology, Hefei, China

    Meng Wang

  5. City University of Hong Kong, Hong Kong, Hong Kong

    Chong-Wah Ngo

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Gu, Y., Xiang, X. (2018). Residual Learning Dehazing Net. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11164. Springer, Cham. https://doi.org/10.1007/978-3-030-00776-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-00776-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00775-1

  • Online ISBN: 978-3-030-00776-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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