  This paper presents a Multispectral imaging (MSI) approach that combines the
use of a diffractive optical element, and a deep learning algorithm for
spectral reconstruction. Traditional MSI techniques often face challenges such
as high costs, compromised spatial or spectral resolution, or prolonged
acquisition times. In contrast, our methodology uses a single diffractive lens,
a grayscale sensor, and an optical motor to capture the Multispectral image
without sacrificing spatial resolution, however with some temporal domain
redundancy. Through an experimental demonstration, we show how we can
reconstruct up to 50 spectral channel images using diffraction physical theory
and a UNet-based deep learning algorithm. This approach holds promise for a
cost-effective, compact MSI camera that could be feasibly integrated into
mobile devices.
