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Area of Science:

  • Optics and Photonics
  • Computational Imaging
  • Spectroscopy

Background:

  • High spectral resolution and snapshot imaging are challenging to achieve simultaneously in thermal infrared spectral imaging.
  • Existing methods often compromise on speed or spectral detail.

Purpose of the Study:

  • To develop a computational imaging method for high spectral resolution thermal infrared imaging with snapshot capability.
  • To enable hyperspectral reconstruction from low-dimensional multispectral measurements for dynamic scenarios.

Main Methods:

  • A self-developed divided-aperture snapshot multispectral camera captures nine low-spectral-resolution images in a single exposure.
  • Star-point array calibration ensures precise registration of sub-channel images.
  • A neural network model reconstructs 127-channel hyperspectral information using a dataset acquired by a Fourier-transform infrared hyperspectral camera (FTIR HCam).

Main Results:

  • The proposed method successfully reconstructs hyperspectral information from multispectral measurements.
  • The system maintains compactness and snapshot imaging capability.
  • Experimental results validate the effectiveness for dynamic thermal infrared sensing.

Conclusions:

  • The developed method offers a viable technical approach for hyperspectral sensing in dynamic thermal infrared scenarios.
  • It overcomes the limitations of simultaneous snapshot imaging and high spectral resolution.
  • This advancement supports applications requiring rapid, detailed spectral analysis in the thermal infrared range.