Abstract Hyperspectral imaging acquires spatially resolved spectral signatures, enabling a wide range of applications from scientific research to industrial processes. Traditional microelectron-mechanical systems (MEMS) Fabry–Pérot (FP) spectrometers offer a compact and simple design but are limited by single free spectral range (FSR) operation. This limitation introduces a fundamental trade-off: achieving high spectral resolution necessitates narrowing the operational bandwidth. Furthermore, maintaining such high resolution demands a larger number of sampling channels, which increases the acquisition time for a single hyperspectral image and thereby limits the frame rate. Here, we present a computational hyperspectral imaging framework that achieves broadband spectral coverage and high frame rate without sacrificing spectral resolution. By dynamically modulating the MEMS-FP cavity to span multiple FSRs, we generate a set of low-correlation spectral sampling patterns as spectral encoders. When combined with a tailored reconstruction algorithm, the system accurately decodes spectral information from a significantly reduced number of sampling channels. We experimentally validate the effectiveness of our system through LED array inspection, demonstrating its potential for high-throughput defect detection in LEDs or screen manufacturing lines. Our work presents a strategy that leverages rapidly advancing computational techniques to overcome the limitations of conventional hardware architectures in hyperspectral imaging. This compact and integrable solution is particularly well-suited for deployment in resource-constrained environments.
Shi et al. (Fri,) studied this question.