Snapshot Multispectral Imaging Using a Diffractive Optical Network

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 achieve snapshot multispectral imaging with 4, 9 and 16 unique spectral bands within the visible spectrum, based on passive spatially-structured diffractive surfaces, with a compact design that axially spans ~72 times the mean wavelength of the spectral band of interest. Moreover, we experimentally demonstrate a diffractive multispectral imager based on a 3D-printed diffractive network that creates at its output image plane a spatially-repeating virtual spectral filter array with 2x2=4 unique bands at terahertz spectrum. Due to their compact form factor and computation-free, power-efficient and polarization-insensitive forward operation, diffractive multispectral imagers can be transformative for various imaging and sensing applications and be used at different parts of the electromagnetic spectrum where high-density and wide-area multispectral pixel arrays are not widely available.