Prior Image Guided Snapshot High-Resolution Spectral Imaging in Near Infrared

Near-Infrared (NIR) hyperspectral imaging opens up numerous possibilities for wide applications. Despite Compressive Spectral Imaging (CSI) being a promising technique, which enables the acquisition of three-dimensional (3D) spatio-spectral information from dynamic scenes, applying it to the NIR spectrum remains challenging. The bottleneck lies in the high cost and limited resolution of InGaAs Focal Plane Arrays (FPAs), which further degrade the high-frequency information of the compressed measurements. Here we demonstrate a novel Effective Prior Image-guided Spectral imager, termed EpiSpec, towards high-resolution spectral imaging in the NIR. Our key observation is that the tail response of low-cost silicon-based sensors tends to capture similar image, offering high spatial resolution guidance for retrieving details. Hence, the degraded measurement of hyperspectral scene, guided by the prior image, is capable of obtaining high-quality reconstructions. Since the prior image integrates only a partial spectrum of the target scene, introducing content-aware chromatic errors, we propose the Prior Image Guided Deep Unfolding Framework (PIUF) for high-fidelity spectral reconstruction. This framework implicitly models the underlying non-linear relationship between the degraded measurements and the Non-Panchromatic (NPA) prior image. We also introduce a new NIR Spectral Images Dataset (NISID), which features a broad selection of real-world NIR spectral interesting scenes. Based on the dataset in hand, we evaluate the sparse structure of such spectra, which can serve as a guide for efficient CSI sensing matrices design. Extensive evaluations on representative CSI systems demonstrate the effectiveness of the proposed EpiSpec framework. Subsequently, lab prototypes are built for real-world imaging validation, further supporting the viability of high-resolution spectral imaging in the NIR.