Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory.

ABSTRACT: Hyperspectral imaging is vital for material identification but traditional systems are bulky, hindering the development of compact systems. While previous metasurfaces address volume issues, the requirements of complicated fabrication processes and significant footprint still limit their applications. This work reports a compact snapshot hyperspectral imager by incorporating the meta-optics with a small-data convex/deep (CODE) deep learning theory. Our snapshot hyperspectral imager comprises only one single multi-wavelength metasurface chip working in the visible window (500-650 nm), significantly reducing the device area. To demonstrate the high performance of our hyperspectral imager, a 4-band multispectral imaging dataset is used as the input. Through the CODE-driven imaging system, it

SUBMITTER: Lin CH 

PROVIDER: S-EPMC10620425 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature