Abstract
Recent hyperspectral imaging has been taking advantage of the development of diffractive optics elements (DOEs) to encode the spectral information in a single or multiple snapshots. Including these diffractive elements, together with previous codification technologies, has allowed the development of compact optical systems at the expense of computational algorithms for recovering spectral information from encoded optical measurements. Diffractive optics design methodologies improve the reconstruction quality by designing point spread functions with reduced dispersion effects. Even more, diffractive optics designs that achieve state-of-the-art performance have been proposed by leveraging available datasets and algorithms based on deep neural networks. This chapter describes the basics for modeling diffractive optical imaging systems and presents compact diffractive optical systems for hyperspectral imaging, the main diffractive optics design methodologies, and the computational algorithms employed to recover the encoded measurements.
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Acknowledgements
The authors acknowledge the Sistema general de regalías Colombia under Grant BPIN 2020000100415 with UIS code 8933 “Desarrollo de un sistema óptico computacional para estimar el contenido de carbono orgánico de suelos agrícolas a traves de imágenes espectrales e inteligencia artificial en cultivos cítricos de Santander.”
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Arguello, H., Galvis, L., Bacca, J., Vargas, E. (2024). Encoded Diffractive Optics for Hyperspectral Imaging. In: Liang, J. (eds) Coded Optical Imaging. Springer, Cham. https://doi.org/10.1007/978-3-031-39062-3_33
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DOI: https://doi.org/10.1007/978-3-031-39062-3_33
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