Key points are not available for this paper at this time.
Hyperspectral (HS) imaging enables the acquisition of color information beyond human perception by utilizing rich spatial-spectral information. However, existing approaches for HS imaging face challenges in practical application due to issues with sensitivity, resolution, and frame rate. In this paper, we report a high-sensitivity and high-resolution HS imaging operating at video-rate (30 fps). The HS imaging is achieved through a compressive-sensing approach using a spatial-spectral coded mask and an image reconstruction process, where the coded mask has spatially and spectrally random transmittance to reconstruct HS images. We defined the randomness required for the coded mask by simulating the effects of spatial and spectral randomness on the reconstruction results. A coded mask satisfying the spatial-spectral randomness were designed by optimizing the structure of Fabry-Pérot resonators and fabricated using a standard semiconductor manufacturing process. The fabricated coded mask was implemented on an image sensor to work as a camera. The experimentally measured sensitivity and spatial resolution are comparable to those of RGB cameras, and the frame rate reaches 30 fps at QVGA resolution with 27 wavelength bands. In addition, implementing in a commerciallyavailable digital camera, we have developed an user-friendly HS imaging with features such as auto-focus, autoexposure, and buttery powered. Our HS imaging, with its high performance and usability, holds great potential for various business scenarios, including consumer applications such as smartphones, drones, and IoT devices.
Yako et al. (Tue,) studied this question.