%0 Conference Proceedings
%T Infrared Image Colorization based on a Triplet DCGAN Architecture
%A Patricia Suarez
%A Angel Sappa
%A Boris X. Vintimilla
%B IEEE Conference on Computer Vision and Pattern Recognition Workshops
%D 2017
%F Patricia Suarez2017
%O ADAS; 600.086; 600.118
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2920), last updated on Tue, 25 Apr 2023 14:09:06 +0200
%X This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all thecolor channels are obtained at the same time.
%U http://refbase.cvc.uab.es/files/SSV2017b.pdf
%U http://dx.doi.org/10.1109/CVPRW.2017.32