Snapshot multi-spectral imaging through defocusing and a Fourier imager network
Journal:
arXiv
Published Date:
Jan 24, 2025
Abstract
Multi-spectral 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.
Here, we introduce a snapshot multi-spectral 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
multi-spectral information; this encoded image information is rapidly decoded
via a deep learning-based multi-spectral Fourier Imager Network (mFIN). We
experimentally tested our method with six illumination bands and demonstrated
an overall accuracy of 92.98% for predicting the illumination channels at the
input and achieved a robust multi-spectral image reconstruction on various test
objects. This deep learning-powered framework achieves high-quality
multi-spectral 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.
