PT Unknown AU Patricia Suarez Angel Sappa Boris X. Vintimilla TI Infrared Image Colorization based on a Triplet DCGAN Architecture BT IEEE Conference on Computer Vision and Pattern Recognition Workshops PY 2017 DI 10.1109/CVPRW.2017.32 AB 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. ER