@InProceedings{ArminMehri2019,
author="Armin Mehri
and Angel Sappa",
title="Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples",
booktitle="IEEE International Conference on Computer Vision and Pattern Recognition-Workshops",
year="2019",
abstract="This paper presents a novel approach for colorizing near infrared (NIR) images. The approach is based on image-to-image translation using a Cycle-Consistent adversarial network for learning the color channels on unpaired dataset. This architecture is able to handle unpaired datasets. The approach uses as generators tailored networks that require less computation times, converge faster and generate high quality samples. The obtained results have been quantitatively---using standard evaluation metrics---and qualitatively evaluated showing considerable improvements with respect to the state of the art",
optnote="MSIAU; 600.130; 601.349; 600.122",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3271), last updated on Wed, 26 Jan 2022 09:35:01 +0100",
opturl="http://openaccess.thecvf.com/content_CVPRW_2019/papers/PBVS/Mehri_Colorizing_Near_Infrared_Images_Through_a_Cyclic_Adversarial_Approach_of_CVPRW_2019_paper.pdf",
file=":http://refbase.cvc.uab.es/files/MeS2019.pdf:PDF"
}