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Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13679))

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Abstract

Images captured under incorrect exposures unavoidably suffer from mixed degradations of lightness and structures. Most existing deep learning-based exposure correction methods separately restore such degradations in the spatial domain. In this paper, we present a new perspective for exposure correction with spatial-frequency interaction. Specifically, we first revisit the frequency properties of different exposure images via Fourier transform where the amplitude component contains most lightness information and the phase component is relevant to structure information. To this end, we propose a deep Fourier-based Exposure Correction Network (FECNet) consisting of an amplitude sub-network and a phase sub-network to progressively reconstruct the representation of lightness and structure components. To facilitate learning these two representations, we introduce a Spatial-Frequency Interaction (SFI) block in two formats tailored to these two sub-networks, which interactively process the local spatial features and the global frequency information to encourage the complementary learning. Extensive experiments demonstrate that our method achieves superior results than other approaches with fewer parameters and can be extended to other image enhancement tasks, validating its potential in wide-range applications. Code will be available at https://github.com/KevinJ-Huang/FECNet.

J. Huang and Y. Liu—Equal contribution.

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Acknowledgments.

This work was supported by the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.

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Authors and Affiliations

  1. University of Science and Technology of China, Hefei, China

    Jie Huang, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou & Zhiwei Xiong

  2. JD Logistics, JD.com, Beijing, China

    Yajing Liu

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  1. Jie Huang
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  3. Feng Zhao
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  5. Jinghao Zhang
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Corresponding authors

Correspondence to Feng Zhao or Man Zhou .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Huang, J. et al. (2022). Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13679. Springer, Cham. https://doi.org/10.1007/978-3-031-19800-7_10

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  • DOI: https://doi.org/10.1007/978-3-031-19800-7_10

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