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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The whole set of image patches used for training and validation, as well as the obtained results, are available by contacting the authors.
References
Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: European Conference on Computer Vision, pp. 577–593. Springer (2016)
Cheng, Z., Yang, Q., Sheng, B.: Deep colorization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 415–423 (2015)
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision, pp. 649–666. Springer (2016)
Oliveira, M., Sappa, A.D., Santos, V.: Unsupervised local color correction for coarsely registered images. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 201–208. IEEE (2011)
Oliveira, M., Sappa, A.D., Santos, V.: A probabilistic approach for color correction in image mosaicking applications. IEEE Trans. Image Process. 24, 508–523 (2015)
Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. In: ACM Transactions on Graphics (TOG), vol. 21, pp. 277–280. ACM (2002)
Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches, arXiv preprint arXiv:1510.05970 (2015)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. CoRR abs/1409.4842 (2014)
Aguilera, C.A., Aguilera, F.J., Sappa, A.D., Aguilera, C., Toledo, R.: Learning cross-spectral similarity measures with deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2016)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color!: joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. In: ACM Transactions on Graphics (Proceedings of SIGGRAPH 2016), p. 35 (2016)
Limmer, M., Lensch, H.: Infrared colorization using deep convolutional neural networks, arXiv preprint arXiv:1604.02245 (2016)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems, pp. 2672–2680 (2014)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2226–2234 (2016)
Brown, M., Süsstrunk, S.: Multi-spectral SIFT for scene category recognition. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 177–184. IEEE (2011)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-61578-3_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61577-6
Online ISBN: 978-3-319-61578-3
eBook Packages: EngineeringEngineering (R0)