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MSF\(^2\)DN: Multi Scale Feature Fusion Dehazing Network with Dense Connection

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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

Single image dehazing is a challenging problem in computer vision. Previous work has mostly focused on designing new encoder and decoder in common network architectures, while neglecting the connection between the two. In this paper, we propose a multi scale feature fusion dehazing network based on dense connection, MSF\(^2\)DN. The design principle of this network is to make full use of dense connection to achieve efficient reuse of features. On the one hand, we use a dense connection inside the base module of the encoder-decoder to fuse the features of different convolutional layers several times, and on the other hand, we design a simple multi-stream feature fusion module which fuses the features of different stages after uniform scaling and feeds them into the base module of the decoder for enhancement. Numerous experiments have demonstrated that our network outperforms the existing state-of-the-art networks.

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Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, Qingdao, China

    Guangfa Wang & Xiaokang Yu

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  1. Guangfa Wang
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  2. Xiaokang Yu
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Correspondence to Xiaokang Yu .

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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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Wang, G., Yu, X. (2023). MSF\(^2\)DN: Multi Scale Feature Fusion Dehazing Network with Dense Connection. 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_27

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  • DOI: https://doi.org/10.1007/978-3-031-26313-2_27

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  • Online ISBN: 978-3-031-26313-2

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