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Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12370))

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Abstract

A novel approach for paired and unpaired image enhancement is proposed in this work. First, we develop global enhancement network (GEN) and local enhancement network (LEN), which can faithfully enhance images. The proposed GEN performs the channel-wise intensity transforms that can be trained easier than the pixel-wise prediction. The proposed LEN refines GEN results based on spatial filtering. Second, we propose different training schemes for paired learning and unpaired learning to train GEN and LEN. Especially, we propose a two-stage training scheme based on generative adversarial networks for unpaired learning. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-arts in paired and unpaired image enhancement. Notably, the proposed unpaired image enhancement algorithm provides better results than recent state-of-the-art paired image enhancement algorithms. The source codes and trained models are available at https://github.com/hukim1124/GleNet.

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Acknowledgements

This work was supported in part by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2020-2016-0–00464) supervised by the IITP (Institute for Information & communications Technology Promotion), in part by the National Research Foundation of Korea (NRF) through the Korea Government (MSIP) under Grant NRF-2018R1A2B3003896, and in part by the research fund of Chungnam National University.

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Authors and Affiliations

  1. Korea University, Seoul, Korea

    Han-Ul Kim & Chang-Su Kim

  2. Chungnam National University, Daejeon, Korea

    Young Jun Koh

Authors
  1. Han-Ul Kim
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  2. Young Jun Koh
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  3. Chang-Su Kim
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Correspondence to Young Jun Koh .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Kim, HU., Koh, Y.J., Kim, CS. (2020). Global and Local Enhancement Networks for Paired and Unpaired Image Enhancement. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12370. Springer, Cham. https://doi.org/10.1007/978-3-030-58595-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-58595-2_21

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  • Publisher Name: Springer, Cham

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