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Neural Color Operators for Sequential Image Retouching

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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

We propose a novel image retouching method by modeling the retouching process as performing a sequence of newly introduced trainable neural color operators. The neural color operator mimics the behavior of traditional color operators and learns pixelwise color transformation while its strength is controlled by a scalar. To reflect the homomorphism property of color operators, we employ equivariant mapping and adopt an encoder-decoder structure which maps the non-linear color transformation to a much simpler transformation (i.e., translation) in a high dimensional space. The scalar strength of each neural color operator is predicted using CNN based strength predictors by analyzing global image statistics. Overall, our method is rather lightweight and offers flexible controls. Experiments and user studies on public datasets show that our method consistently achieves the best results compared with SOTA methods in both quantitative measures and visual qualities. Code is available at https://github.com/amberwangyili/neurop.

Work done during Yili Wang’s internship at VIS, Baidu.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Project Number: 61932003).

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Authors and Affiliations

  1. BNRist, Department of CS &T, Tsinghua University, Beijing, China

    Yili Wang & Kun Xu

  2. Department of Computer Vision Technology (VIS), Baidu Inc., Beijing, China

    Xin Li, Dongliang He, Qi Zhang, Fu Li & Errui Ding

Authors
  1. Yili Wang
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  2. Xin Li
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  3. Kun Xu
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  4. Dongliang He
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  5. Qi Zhang
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  6. Fu Li
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  7. Errui Ding
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Corresponding author

Correspondence to Kun Xu .

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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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Wang, Y. et al. (2022). Neural Color Operators for Sequential Image Retouching. 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_3

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  • DOI: https://doi.org/10.1007/978-3-031-19800-7_3

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