Representative Color Transform for Image Enhancement

Hanul Kim, Su Min Choi, Chang Su Kim, Yeong Jun Koh

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

38 Citations (Scopus)


Recently, the encoder-decoder and intensity transformation approaches lead to impressive progress in image enhancement. However, the encoder-decoder often loses details in input images during down-sampling and up-sampling processes. Also, the intensity transformation has a limited capacity to cover color transformation between low-quality and high-quality images. In this paper, we propose a novel approach, called representative color transform (RCT), to tackle these issues in existing methods. RCT determines different representative colors specialized in input images and estimates transformed colors for the representative colors. It then determines enhanced colors using these transformed colors based on the similarity between input and representative colors. Extensive experiments demonstrate that the proposed algorithm outperforms recent state-of-the-art algorithms on various image enhancement problems.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781665428125
Publication statusPublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
Duration: 2021 Oct 112021 Oct 17

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
ISSN (Print)1550-5499


Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
CityVirtual, Online

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No.NRF-2018R1A2B3003896, No. NRF-2019R1F1A1062907, and No. NRF-2021R1A4A1031864)

Publisher Copyright:
© 2021 IEEE

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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