Snapshot multispectral imaging through defocusing and a Fourier imager network

Multispectral imaging, which simultaneously captures the spatial and spectral information of a scene, is widely used across diverse fields, including remote sensing, biomedical imaging, and agricultural monitoring. We introduce a snapshot multispectral imaging approach employing a standard monochrome image sensor with no additional spectral filters or customized components. Our system leverages the inherent chromatic aberration of wavelength-dependent defocusing as a natural source of physical encoding of multispectral information; this encoded image information is rapidly decoded via a deep learning-based multispectral Fourier imager network (mFIN). We experimentally tested our method with six illumination bands and demonstrated an overall accuracy of 98.25% for predicting the illumination channels at the input and achieved a robust multispectral image reconstruction on various test objects. This deep learning-powered framework achieves high-quality multispectral image reconstruction using snapshot image acquisition with a monochrome image sensor and could be useful for applications in biomedicine, industrial quality control, and agriculture, among others.