Infrared Image Colorization Based on a Triplet DCGAN Architecture

Abstract

This paper proposes a novel approach for colorizing near infrared (NIR) images using a Deep Convolutional Generative Adversarial Network (GAN) architecture. 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 colored 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 where all the color channels are obtained at the same time.