TY - CONF AU - Patricia Suarez AU - Angel Sappa AU - Boris X. Vintimilla A2 - ICIAP PY - 2017// TI - Colorizing Infrared Images through a Triplet Conditional DCGAN Architecture BT - 19th international conference on image analysis and processing KW - CNN in Multispectral Imaging KW - Image Colorization N2 - This paper focuses on near infrared (NIR) image colorization by using a Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) architecture model. The proposed architecture is based on the usage of a conditional probabilistic generative model. Firstly, it learns to colorize the given input image, by using a triplet model architecture that tackle every channel in an independent way. In the proposed model, the nal layer of red channel consider the infrared image to enhance the details, resulting in a sharp RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. Experimental results with a large set of real images are provided showing the validity of the proposed approach. Additionally, the proposed approach is compared with a state of the art approach showing better results. L1 - http://refbase.cvc.uab.es/files/SSV2017c.pdf N1 - ADAS; MSIAU; 600.086; 600.122; 600.118 ID - Patricia Suarez2017 ER -