%0 Conference Proceedings
%T Colorizing Near Infrared Images through a Cyclic Adversarial Approach of Unpaired Samples
%A Armin Mehri
%A Angel Sappa
%B IEEE International Conference on Computer Vision and Pattern Recognition-Workshops
%D 2019
%F Armin Mehri2019
%O MSIAU; 600.130; 601.349; 600.122
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3271), last updated on Wed, 26 Jan 2022 09:35:01 +0100
%X 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
%U http://openaccess.thecvf.com/content_CVPRW_2019/papers/PBVS/Mehri_Colorizing_Near_Infrared_Images_Through_a_Cyclic_Adversarial_Approach_of_CVPRW_2019_paper.pdf
%U http://refbase.cvc.uab.es/files/MeS2019.pdf