Compressive Hyperspectral Imaging Based on an End-to-end Learned Metalens

F. Kobayashi; Masashi Miyata; Yoko Sogabe; Toshikazu Hashimoto

doi:10.1364/cleo_at.2022.jw5q.6

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Compressive Hyperspectral Imaging Based on an End-to-end Learned Metalens

F. Kobayashi Masashi Miyata Yoko Sogabe Toshikazu Hashimoto

Year: 2022 Journal: Conference on Lasers and Electro-Optics Vol: 6 Pages: JW5Q.6-JW5Q.6

DOI: 10.1364/cleo_at.2022.jw5q.6

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Abstract

We design a compressive hyperspectral imaging system by end-to-end optimization of a metalens and image-reconstruction neural network. Our system shows superior spectral-spatial image quality to systems using a standard metalens and neural network.

Keywords:

Hyperspectral imaging Compressed sensing Full spectral imaging End-to-end principle Computer science Artificial intelligence Artificial neural network Image quality Iterative reconstruction Computer vision Spectral imaging Image (mathematics) Remote sensing Geology

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Random lasers and scattering media

Physical Sciences → Physics and Astronomy → Acoustics and Ultrasonics

Optical Polarization and Ellipsometry

Physical Sciences → Engineering → Biomedical Engineering

Metamaterials and Metasurfaces Applications

Physical Sciences → Materials Science → Electronic, Optical and Magnetic Materials

Compressive Hyperspectral Imaging Based on an End-to-end Learned Metalens

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