Snapshot Multispectral Imaging Using a Diffractive Optical Network
Deniz Mengu, Anika Tabassum, Mona Jarrahi, Aydogan Ozcan

TL;DR
This paper introduces a diffractive optical network for multispectral imaging that creates virtual spectral filters, enabling compact, power-efficient, and polarization-insensitive multispectral imaging across visible and terahertz spectra without traditional filters.
Contribution
The work presents a novel deep learning-trained diffractive optical network that performs snapshot multispectral imaging with multiple spectral bands, eliminating the need for spectral filters or complex image recovery algorithms.
Findings
Achieved multispectral imaging with 4, 9, and 16 spectral bands in simulations.
Experimentally demonstrated a 4-band terahertz multispectral imager using 3D-printed diffractive network.
Designed a compact, polarization-insensitive multispectral imaging system with broad applications.
Abstract
Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine. Here, we present a diffractive optical network-based multispectral imaging system trained using deep learning to create a virtual spectral filter array at the output image field-of-view. This diffractive multispectral imager performs spatially-coherent imaging over a large spectrum, and at the same time, routes a pre-determined set of spectral channels onto an array of pixels at the output plane, converting a monochrome focal plane array or image sensor into a multispectral imaging device without any spectral filters or image recovery algorithms. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. Through numerical simulations, we present different diffractive network designs that…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Optical Polarization and Ellipsometry · Photonic and Optical Devices
