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PieNet: Personalized Image Enhancement Network

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12375))

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Abstract

Image enhancement is an inherently subjective process since people have diverse preferences for image aesthetics. However, most enhancement techniques pay less attention to the personalization issue despite its importance. In this paper, we propose the first deep learning approach to personalized image enhancement, which can enhance new images for a new user, by asking him or her to select about 10–20 preferred images from a random set of images. First, we represent various users’ preferences for enhancement as feature vectors in an embedding space, called preference vectors. We construct the embedding space based on metric learning. Then, we develop the personalized image enhancement network (PieNet) to enhance images adaptively using each user’s preference vector. Experimental results demonstrate that the proposed algorithm is capable of achieving personalization successfully, as well as outperforming conventional general image enhancement algorithms significantly. The source codes and trained models are available at https://github.com/hukim1124/PieNet.

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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

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  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). PieNet: Personalized Image Enhancement Network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12375. Springer, Cham. https://doi.org/10.1007/978-3-030-58577-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-58577-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58576-1

  • Online ISBN: 978-3-030-58577-8

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