![]() One of the simplest ways to achieve multispectral imaging is to sacrifice the image acquisition time in favor of the spectral information by capturing multiple shots of a scene while changing the spectral filter in front of a monochrome camera 21. Multispectral imaging has been an instrumental tool for major advances in various fields, including environmental monitoring 1, astronomy 2, 3, 4, agricultural sciences 5, 6, biological imaging 7, 8, 9, medical diagnostics 10, 11, and food quality control 12, 13 among many others 14, 15, 16, 17, 18, 19, 20. 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. 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 2 × 2 = 4 unique bands at terahertz spectrum. 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 λ m, where λ m is the mean wavelength of the spectral band of interest. Furthermore, the spectral responsivity of this diffractive multispectral imager is not sensitive to input polarization states. 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. 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. Multispectral imaging has been used for numerous applications in e.g., environmental monitoring, aerospace, defense, and biomedicine.
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