W-net: Two-stage U-net with misaligned data for raw-to-RGB mapping

Kwang Hyun Uhm, Seung Wook Kim, Seo Won Ji, Sung Jin Cho, Jun Pyo Hong, Sung Jea Ko

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recent research on a learning mapping between raw Bayer images and RGB images has progressed with the development of deep convolutional neural network. A challenging data set namely the Zurich Raw-to-RGB data set (ZRR) has been released in the AIM 2019 raw-to-RGB mapping challenge. In ZRR, input raw and target RGB images are captured by two different cameras and thus not perfectly aligned. Moreover, camera metadata such as white balance gains and color correction matrix are not provided, which makes the challenge more difficult. In this paper, we explore an effective network structure and a loss function to address these issues. We exploit a two-stage U-Net architecture, and also introduce a loss function that is less variant to alignment and more sensitive to color differences. In addition, we show an ensemble of networks trained with different loss functions can bring a significant performance gain. We demonstrate the superiority of our method by achieving the highest score in terms of both the peak signal-to-noise ratio and the structural similarity and obtaining the second-best mean-opinion-score in the challenge.

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3636-3642
Number of pages7
ISBN (Electronic)9781728150239
DOIs
Publication statusPublished - 2019 Oct
Event17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of
Duration: 2019 Oct 272019 Oct 28

Publication series

NameProceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
CountryKorea, Republic of
CitySeoul
Period19/10/2719/10/28

Keywords

  • Image enhancement
  • Image restoration
  • Raw to RGB mapping

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Uhm, K. H., Kim, S. W., Ji, S. W., Cho, S. J., Hong, J. P., & Ko, S. J. (2019). W-net: Two-stage U-net with misaligned data for raw-to-RGB mapping. In Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 (pp. 3636-3642). [9022089] (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2019.00448