Towards Ultra High-Speed Hyperspectral Imaging by Integrating Compressive and Neuromorphic Sampling

Abstract

Hyperspectral and high-speed imaging are both important for scene representation and understanding. However, simultaneously capturing both hyperspectral and high-speed data is still under-explored. In this work, we propose a high-speed hyperspectral imaging system by integrating compressive sensing sampling with bioinspired neuromorphic sampling. Our system includes a coded aperture snapshot spectral imager capturing moderate-speed hyperspectral measurement frames and a spike camera capturing high-speed grayscale dense spike streams. The two cameras provide complementary dual-modality data for reconstructing high-speed hyperspectral videos (HSV). To effectively synergize the two sampling mechanisms and obtain high-quality HSV, we propose a unified multi-modal reconstruction framework. The framework consists of a Spike Spectral Prior Network for spike-based information extraction and prior regularization, coupled with a dual-modality iterative optimization algorithm for reliable reconstruction. We finally build a hardware prototype to verify the effectiveness of our system and algorithm design. Experiments on both simulated and real data demonstrate the superiority of the proposed approach, where for the first time to our knowledge, high-speed HSV with 30 spectral bands can be captured at a frame rate of up to 20,000 FPS.