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

Snapshot hyperspectral imaging based on metasurface optical filters combined with computational spectral reconstruction offers extensive applicability for miniature and compact spectral systems. However, existing designs are constrained by the angular dispersion response of metasurfaces and the limited cross-correlation among transmission spectra, leading to imperfect reconstruction and application challenges. In this paper, we propose a method for angle-robust hyperspectral imaging based on quasi-random metal metasurfaces that enables a 40° field-of-view within the 500-700 nm wavelength range. Furthermore, we introduce a cross-correlation optimization technique based on an orthogonal matrix to align with the principle of compressed sensing theory, achieving a cross-correlation of 0.49 with an average angle sensitivity of 1.11%. The results show angle-robust hyperspectral reconstruction with an average spectral fidelity of 91.64% for computational spectrometers, along with excellent performance for hyperspectral imaging. This advancement enhances the accuracy of hyperspectral reconstruction for large field-of-view spectral devices and close-up analysis, demonstrating substantial potential for integration into portable spectral devices.