Computational Hyperspectral Camera Design Based on Co-Modulation of Diffractive Optical Element and Superposition Fabry-Perot Filter Array

Encoded computational hyperspectral cameras, propelled by advances in compressed sensing theory, making both miniaturization and real-time hyperspectral imaging feasible. Spectral-encoded or spatial-encoded hyperspectral imaging strategy have limited numbers of design parameters in optical components, leading to severe ill-posedness in hyperspectral images reconstruction, which constrain overall imaging quality. However, spatial-spectral-encoded hyperspectral imaging strategy which simultaneously performs spatial and spectral encoding entailing more powerful modulation, alleviating ill-posed problems and improving the quality of hyperspectral images. In this paper, we present a co-modulation framework based on diffractive optical element (DOE) and Superposition Fabry&#x2013;Perot (SFP) filter array for computational hyperspectral camera that integrates these two components with a transformer-based reconstruction network through end-to-end learning. The learned DOE and SFP filter encode the hyperspectral datacube on the sensor via phase and amplitude modulation, and the transformer-based network accurately reconstructs the images from sensor measurements. We conduct extensive simulations to analyze and validate the relatively contributions of the DOE, SFP filter, and transformer-based reconstruction algorithm to the significantly improved performance of hyperspectral image reconstruction across various ablation study models. We further investigate and identify the <inline-formula><tex-math notation="LaTeX">$\mathbf {4\times 4}$</tex-math></inline-formula> SFP filter unit configuration as the most effective design for achieving a balance between spectral fidelity and spatial resolution. Our results show that the proposed system outperforms state-of-the-art methods in hyperspectral images reconstruction quality, excelling in both spatial and spectral detail recovery, and maintaining good performance against realistic noise levels.