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

Chia Hsiang Lin, Shih Hsiu Huang, Ting Hsuan Lin, Pin Chieh Wu

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

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 efficiently generates an 18-band hyperspectral data cube with high fidelity using only 18 training data points. We expect the elegant integration of multi-resonant metasurfaces with small-data learning theory will enable low-profile advanced instruments for fundamental science studies and real-world applications.

Original languageEnglish
Article number6979
JournalNature communications
Volume14
Issue number1
DOIs
Publication statusPublished - 2023 Dec

All Science Journal Classification (ASJC) codes

  • General Chemistry
  • General Biochemistry,Genetics and Molecular Biology
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory'. Together they form a unique fingerprint.

Cite this