@inproceedings{alvarez-gila_adversarial_2017,
title = {Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB},
author = {AITOR ALVAREZ-GILA and JOOST VAN DE WEIJER and ESTÍBALIZ GARROTE},
url = {https://arxiv.org/abs/1709.00265},
doi = {10.1109/ICCVW.2017.64},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE International Conference on Computer Vision Workshops (ICCVW)},
pages = {480--490},
abstract = {Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.},
note = {The arxiv.org version contains updated results.},
keywords = {cnn, color, GAN, hyperspectral, neural networks},
pubstate = {published},
tppubtype = {inproceedings}
}
Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to build informative priors from real world object reflectances for constructing such RGB to spectral signal mapping. However, most of them treat each sample independently, and thus do not benefit from the contextual information that the spatial dimensions can provide. We pose hyperspectral natural image reconstruction as an image to image mapping learning problem, and apply a conditional generative adversarial framework to help capture spatial semantics. This is the first time Convolutional Neural Networks -and, particularly, Generative Adversarial Networks- are used to solve this task. Quantitative evaluation shows a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0% on the ICVL natural hyperspectral image dataset.
@article{garrote2011algorithms,
title = {Algorithms for colour image processing based on neurological models},
author = {Estibaliz Garrote-Contreras},
year = {2011},
date = {2011-01-01},
publisher = {Servicio Editorial de la Universidad del País Vasco/Euskal Herriko Unibertsitatearen Argitalpen Zerbitzua},
keywords = {color, image processing},
pubstate = {published},
tppubtype = {article}
}
@conference{Olabarria2004bb,
title = {Reciclado Avanzado mediante tecnologías de proceso de imagen},
author = {Jose A. Gutiérrez-Olabarria and Estibaliz Garrote-Contreras and Arantza Bereciartua-Pérez and Artzai Picón-Ruiz},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2004_ISR-Tecnologias-Vision-Artificial.pdf
},
year = {2004},
date = {2004-02-17},
booktitle = {NI- Days 2004},
abstract = {El desarrollo sostenible y las normativas cada vez más exigentes de la UE hacen imprescindible encontrar nuevos métodos y sistemas no contaminantes que permitan optimizar el reciclado actual de materiales y que resuelvan nuevas aplicaciones de reciclado, de manera que se minimicen los residuos y se controlen aquellas sustancias peligrosas. La visión artificial (convencional, color y multiespectral) ofrece soluciones flexibles y potentes para este tipo de aplicaciones.},
keywords = {color, machine vision, recycling},
pubstate = {published},
tppubtype = {conference}
}
El desarrollo sostenible y las normativas cada vez más exigentes de la UE hacen imprescindible encontrar nuevos métodos y sistemas no contaminantes que permitan optimizar el reciclado actual de materiales y que resuelvan nuevas aplicaciones de reciclado, de manera que se minimicen los residuos y se controlen aquellas sustancias peligrosas. La visión artificial (convencional, color y multiespectral) ofrece soluciones flexibles y potentes para este tipo de aplicaciones.
@article{ruiz2004high,
title = {High resolution adaptive colour printing verification system for quality control of distorted elements.},
author = {Artzai Picón-Ruiz and Estibaliz Garrote-Contreras and Jose A. Gutiérrez-Olabarria and EVARISTO KAHORAHO BUKUBILLE and JOSE IGNACIO LARRAURI VILLAMOR and A ANTTON},
url = {https://computervision.tecnalia.com/wp-content/uploads/2012/12/2004_IntelligentSystem.pdf},
year = {2004},
date = {2004-01-01},
journal = {WSEAS Transactions on Circuits and Systems},
volume = {3},
number = {10},
pages = {2205--2210},
keywords = {color, image processing, quality control},
pubstate = {published},
tppubtype = {article}
}
