TY  - CONF
AU  - Patricia Suarez
AU  - Angel Sappa
AU  - Boris X. Vintimilla
A2  - PAAMS
PY  - 2017//
TI  - Learning to Colorize Infrared Images
BT  - 15th International Conference on Practical Applications of Agents and Multi-Agent System
KW  - CNN in multispectral imaging
KW  - Image colorization
N2  - This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a 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. The proposed model starts the learning process from scratch, because our set of images is very dierent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
UR  - https://doi.org/10.1007/978-3-319-61578-3_16
L1  - http://refbase.cvc.uab.es/files/SSV2017d.pdf
N1  - ADAS; MSIAU; 600.086; 600.122; 600.118
ID  - Patricia Suarez2017
ER  -