  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.
