Reconstructing spectral images from RGB-images using a convolutional neural network
Stiebel T, Koppers S, Seltsam P, Merhof D (2018)
Publication Type: Conference contribution
Publication year: 2018
Publisher: IEEE Computer Society
Book Volume: 2018-June
Pages Range: 1061-1066
Conference Proceedings Title: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Event location: Salt Lake City, UT, USA
ISBN: 9781538661000
Abstract
Recovering high-dimensional spectral images taken with spectrally low-dimensional camera systems, in the extreme case RGB-images, has been of great interest for a variety of applications. An accurate spectral reconstruction is typically required to either achieve a better color accuracy or to improve object recognition/classification tasks. Almost all published work to date aims at performing a mapping from individual camera signals towards the corresponding spectrum. However, it might be beneficial to consider not only single pixels, but also contextual information. Here, we propose a convolutional neural network architecture that learns a mapping from RGB-to spectral images. We trained the network on the largest hyperspectral data set available to date [3] and analyzed the influence of different error metrics as loss functions. An objective evaluation of the performance in comparison to state of the art spectral reconstruction techniques is given by participating in the NTIRE 2018 challenge on spectral reconstruction [4].
Involved external institutions
Germany (DE)How to cite
APA:
Stiebel, T., Koppers, S., Seltsam, P., & Merhof, D. (2018). Reconstructing spectral images from RGB-images using a convolutional neural network. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 1061-1066). Salt Lake City, UT, USA: IEEE Computer Society.
MLA:
Stiebel, Tarek, et al. "Reconstructing spectral images from RGB-images using a convolutional neural network." Proceedings of the 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018, Salt Lake City, UT, USA IEEE Computer Society, 2018. 1061-1066.
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