Abstract
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 different 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.
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The whole set of image patches used for training and validation, as well as the obtained results, are available by contacting the authors.
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Acknowledgments
This work has been partially supported by the ESPOL projects: “Pattern recognition: case study on agriculture and aquaculture” (M1-DI-2015) and “Integrated system for emergency management using sensor networks and reactive signaling” (G4-DI-2014); and by the Spanish Government under Project TIN2014-56919-C3-2-R.
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Suárez, P.L., Sappa, A.D., Vintimilla, B.X. (2018). Learning to Colorize Infrared Images. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_16
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DOI: https://doi.org/10.1007/978-3-319-61578-3_16
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