High-Spatial and Spectral Resolution Snapshot Spectral Imaging Based on Nearfield Coupled Metasurfaces

Abstract

Snapshot spectral imaging based on metasurfaces provides spatial and spectral information, making it highly promising for consumer applications including drones, smartphones, and Internet of Things devices (IoT). However, current metasurface-based spectral imaging techniques rely on nonlocal coupling, i.e., collective resonance, which requires significantly large periodic structures to achieve narrow line width response, resulting in a trade-off between spectral and spatial resolution. Herein, we theoretically propose and demonstrate a narrow line width spatial-spectral metasurface encoder based on localized coupling, achieving high spatial and spectral resolution simultaneously. By employing a global optimization algorithm to optimize the coupling strength between adjacent nanoblocks, the footprint of a spatial-spectral metasurface encoder can be reduced to as small as ∼5 μm, which is comparable to the pixel size of the CMOS imager. By combining deep learning reconstruction algorithms, we can achieve high-precision spectral recovery in the visible range across various spectral images. Our approach offers valuable insights for high-speed, high spatial, and spectral resolution spectral imaging.