RT Conference Proceedings
T1 Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB
A1 Alvarez-Gila, Aitor
A1 Van de Weijer, Joost
A1 Garrote, Estibaliz
AB 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.
PB IEEE
SN 9781538610343
YR 2017
FD 2017-10
LA eng
NO Alvarez-Gila , A , Van de Weijer , J & Garrote , E 2017 , Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB . in unknown . 2018-January , IEEE , pp. 480-490 , 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 , Venice , Italy , 22/10/17 . https://doi.org/10.1109/ICCVW.2017.64
NO conference
NO Publisher Copyright: © 2017 IEEE.
DS TECNALIA Publications
RD 29 sept 2024