Mid-wave infrared (MWIR) hyperspectral detection enables a significant enhancement in target camouflage recognition capability. Methods based on computational spectroscopy demonstrate absolute predominance in real-time performance, detection range, and spatial resolution. This work presents an MWIR GaSb-substrate metallic metasurface compatible with a single-step lift-off fabrication process for hyperspectral computational spectral imaging. All simulations and experiments in this paper were conducted under the condition of 0° linearly polarized incident light. The finite-difference time-domain (FDTD) method is utilized to simulate the transmission spectra of 36 metasurface unit cells, construct the observation matrices, and analyze their correlation coefficients and energy utilization efficiency. Through a greedy algorithm, 11 low-correlation structures are screened, reducing the average correlation coefficient from 0.539 to 0.376 while boosting the energy utilization efficiency to 63.6%, thereby remarkably enhancing the system’s compressive sensing performance. The basis pursuit algorithm is employed for reconstructing Gaussian and complex sparse spectral signals, revealing that the co-design of metasurface structural optimization and compressed sensing algorithms plays a pivotal role in improving the performance of miniature spectrometers. This paper provides a viable pathway for the development of portable spectral imaging systems for complex environments, with extensive application prospects in fields including environmental monitoring, food safety, and biomedical engineering.
CHAO et al. (Mon,) studied this question.