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Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture

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Advances in Visual Computing (ISVC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13018))

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Abstract

This paper presents a novel attention based architecture to remove non-homogeneous haze. The proposed model is focused on obtaining the most representative characteristics of the image, at each learning cycle, by means of adaptive attention modules coupled with a residual learning convolutional network. The latter is based on the Res2Net model. The proposed architecture is trained with just a few set of images. Its performance is evaluated on a public benchmark—images from the non-homogeneous haze NTIRE 2021 challenge—and compared with state of the art approaches reaching the best result.

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Acknowledgements

This work has been partially supported by the ESPOL Polytechnic University; the Spanish Government under Project TIN2017-89723-P; and the “CERCA Programme/Generalitat de Catalunya”. The authors gratefully acknowledge the support of the CYTED Network: “Ibero-American Thematic Network on ICT Applications for Smart Cities” (REF-518RT0559) and the NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

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Authors and Affiliations

  1. ESPOL Polytechnic University, FIEC, CIDIS, Guayaquil, Ecuador

    Patricia L. Suárez, Dario Carpio & Angel D. Sappa

  2. Computer Vision Center, 08193, Bellaterra, Barcelona, Spain

    Angel D. Sappa

Authors
  1. Patricia L. Suárez
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  2. Dario Carpio
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  3. Angel D. Sappa
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Corresponding author

Correspondence to Patricia L. Suárez .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Texas at Arlington, Arlington, TX, USA

    Vassilis Athitsos

  3. University of South Carolina, Columbia, SC, USA

    Tong Yan

  4. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  5. School of Engineering and Computing, University of Durham, Durham, Durham, UK

    Frederick Li

  6. Airbnb, New York, NY, USA

    Conglei Shi

  7. Peking University, Beijing, China

    Xiaoru Yuan

  8. Purdue University, West Lafayette, IN, USA

    Christos Mousas

  9. IST, School of Modeling, Simulation, and Training, Orlando, FL, USA

    Gerd Bruder

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Suárez, P.L., Carpio, D., Sappa, A.D. (2021). Non-homogeneous Haze Removal Through a Multiple Attention Module Architecture. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_14

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  • DOI: https://doi.org/10.1007/978-3-030-90436-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90435-7

  • Online ISBN: 978-3-030-90436-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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