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Graph Disentangled Representation Based Semi-supervised Single Image Dehazing Network

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14087))

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Abstract

Single image dehazing algorithms aim to recover a clear image from a hazy one. Most learning-based single image dehazing algorithms are trained on synthetic datasets and have limited inference ability to real-world scenes. We propose a graph-disentangled representation based semi-supervised single image dehazing algorithm (GDSDN). Specifically, a graph-disentangled representation network is presented to decouple the content and mask features, and the decoupled content features are employed to reconstruct dehazed results. In addition, the interaction-reconstruction strategy and contrastive loss are designed to constrain the disentangled content and mask features. Extensive experimental results on synthetic and real-world images show that our model achieves competitive results.

This work in this paper is supported by the Beijing Natural Science Foundation (No. L211017), the General Program of Beijing Municipal Education Commission (No. KM202110005027), the National Natural Science Foundation of China (No. 61971016), and the R&D Program of Beijing Municipal Education Commission (No. KZ202210005007)

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Tongyao Jia, Jiafeng Li & Li Zhuo

  2. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing, 100124, China

    Jiafeng Li & Li Zhuo

Authors
  1. Tongyao Jia
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  2. Jiafeng Li
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  3. Li Zhuo
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Corresponding authors

Correspondence to Jiafeng Li or Li Zhuo .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Jia, T., Li, J., Zhuo, L. (2023). Graph Disentangled Representation Based Semi-supervised Single Image Dehazing Network. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_54

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  • DOI: https://doi.org/10.1007/978-981-99-4742-3_54

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4741-6

  • Online ISBN: 978-981-99-4742-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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