TY - GEN
T1 - Near-Infrared Image Colorization with Weighted UNet++ and Auxiliary Color Enhancement GAN
AU - Zhou, Sicong
AU - Kamata, Sei Ichiro
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - We propose a novel GAN-based method for near-infrared image colorization. This method innovatively rebalances the color of the colorization image by importing a luminance channel and a feature weight-driven color generator. We set the weighted UNet++ structure in the generator for colorization results with the detail of focal objects. A color enhancement network composed of a deeper luminance network and a colorimetric network is used for global color balance to improve the color quality of the generated color images. Our network is trained and evaluated on two datasets. According to the FID, SSIM and PSNR results, our network performs well, with good recovery effects for both overall color and detailed color and outperforming the current state-of-the-art methods.
AB - We propose a novel GAN-based method for near-infrared image colorization. This method innovatively rebalances the color of the colorization image by importing a luminance channel and a feature weight-driven color generator. We set the weighted UNet++ structure in the generator for colorization results with the detail of focal objects. A color enhancement network composed of a deeper luminance network and a colorimetric network is used for global color balance to improve the color quality of the generated color images. Our network is trained and evaluated on two datasets. According to the FID, SSIM and PSNR results, our network performs well, with good recovery effects for both overall color and detailed color and outperforming the current state-of-the-art methods.
KW - Colorization
KW - Generative Adversarial Network
KW - Luminance
KW - Near-Infrared
KW - UNet++
UR - http://www.scopus.com/inward/record.url?scp=85139516853&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139516853&partnerID=8YFLogxK
U2 - 10.1109/ICIVC55077.2022.9887040
DO - 10.1109/ICIVC55077.2022.9887040
M3 - Conference contribution
AN - SCOPUS:85139516853
T3 - 2022 7th International Conference on Image, Vision and Computing, ICIVC 2022
SP - 507
EP - 512
BT - 2022 7th International Conference on Image, Vision and Computing, ICIVC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Image, Vision and Computing, ICIVC 2022
Y2 - 26 July 2022 through 28 July 2022
ER -