@InProceedings{PatriciaSuarez2018,
author="Patricia Suarez
and Angel Sappa
and Boris X. Vintimilla
and Riad I. Hammoud",
title="Near InfraRed Imagery Colorization",
booktitle="25th International Conference on Image Processing",
year="2018",
pages="2237--2241",
optkeywords="Convolutional Neural Networks (CNN)",
optkeywords="Generative Adversarial Network (GAN)",
optkeywords="Infrared Imagery colorization",
abstract="This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multipleloss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics.",
optnote="MSIAU; 600.086; 600.130; 600.122",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3195), last updated on Tue, 25 Apr 2023 14:10:35 +0200",
doi="10.1109/ICIP.2018.8451413",
file=":http://refbase.cvc.uab.es/files/SSV2018b.pdf:PDF"
}