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SG-Net: Semantic Guided Network for Image Dehazing

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13843))

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Abstract

From traditional handcrafted priors to learning-based neural networks, image dehazing technique has gone through great development. In this paper, we propose an end-to-end Semantic Guided Network (SG-Net (Codebase page: https://github.com/PaulTHong/Dehaze-SG-Net)) for directly restoring the haze-free images. Inspired by the high similarity (mapping relationship) between the transmission maps and the segmentation results of hazy images, we found that the semantic information of the scene provides a strong natural prior for image restoration. To guide the dehazing more effectively and systematically, we utilize the information of semantic segmentation with three easily portable modes: Semantic Fusion (SF), Semantic Attention (SA), and Semantic Loss (SL), which compose our Semantic Guided (SG) mechanisms. By embedding these SG mechanisms into existing dehazing networks, we construct the SG-Net series: SG-AOD, SG-GCA, SG-FFA, and SG-AECR. The outperformance on image dehazing of these SG networks is demonstrated by the experiments in terms of both quantity and quality. It is worth mentioning that SG-FFA achieves the state-of-the-art performance.

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Acknowledgements

This work was supported by the Natural Science Foundation of China under grant 62071171.

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Authors and Affiliations

  1. Department of Information and Computational Sciences, School of Mathematical Sciences and LMAM, Peking University, Beijing, 100871, China

    Tao Hong, Xiangyang Guo, Zeren Zhang & Jinwen Ma

Authors
  1. Tao Hong
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  2. Xiangyang Guo
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  3. Zeren Zhang
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  4. Jinwen Ma
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Corresponding author

Correspondence to Jinwen Ma .

Editor information

Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Hong, T., Guo, X., Zhang, Z., Ma, J. (2023). SG-Net: Semantic Guided Network for Image Dehazing. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13843. Springer, Cham. https://doi.org/10.1007/978-3-031-26313-2_17

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26312-5

  • Online ISBN: 978-3-031-26313-2

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