Angle-robust hyperspectral imaging based on quasi-random metal metasurfaces and cross-correlation optimization

Area of Science:

• Optics and Photonics
• Computational Imaging
• Materials Science

Background:

• Snapshot hyperspectral imaging utilizes metasurface filters and computational reconstruction for compact systems.
• Existing methods face limitations due to metasurface angular dispersion and low spectral cross-correlation, hindering reconstruction accuracy.

Purpose of the Study:

• To develop an angle-robust hyperspectral imaging method for miniature spectral systems.
• To improve hyperspectral reconstruction accuracy and expand the field-of-view for computational spectrometers.

Main Methods:

• Employed quasi-random metal metasurfaces for angle-robustness.
• Implemented a cross-correlation optimization technique using an orthogonal matrix, inspired by compressed sensing.
• Achieved a 40° field-of-view within the 500-700 nm wavelength range.

Main Results:

• Demonstrated angle-robust hyperspectral reconstruction with an average spectral fidelity of 91.64%.
• Attained a cross-correlation of 0.49 with an average angle sensitivity of 1.11%.
• Validated excellent performance for hyperspectral imaging applications.

Conclusions:

• The proposed method significantly enhances hyperspectral reconstruction accuracy for wide field-of-view and close-up spectral devices.
• This advancement holds substantial potential for integration into portable spectral devices.
• The technique addresses key challenges in current metasurface-based hyperspectral imaging.