TY - GEN
T1 - Representative Color Transform for Image Enhancement
AU - Kim, Hanul
AU - Choi, Su Min
AU - Kim, Chang Su
AU - Koh, Yeong Jun
N1 - 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
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122054685&partnerID=8YFLogxK
U2 - 10.1109/ICCV48922.2021.00442
DO - 10.1109/ICCV48922.2021.00442
M3 - Conference contribution
AN - SCOPUS:85122054685
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 4439
EP - 4448
BT - Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
Y2 - 11 October 2021 through 17 October 2021
ER -