Snapshot multi-spectral imaging through defocusing and a Fourier imager network
This provides a low-cost, snapshot imaging solution for fields like biomedicine and agriculture, though it is incremental as it builds on existing defocusing and deep learning techniques.
The paper tackles snapshot multi-spectral imaging by using wavelength-dependent defocusing and a deep learning network, achieving 92.98% accuracy in predicting illumination channels and robust image reconstruction.
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.