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

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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.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Notes

  1. For example, a Phantom v2640 camera (maximum 18,120 FPS at 480 p) has a size of 28 \(\times \) 19 \(\times \) 18.88 cm, a weight of 8.1 kg, and costs more than $100,000. During imaging, it generates nearly 37 GB of data per second.

  2. Please refer to the supplementary material for more details on why a DVS sensor is not selected for our work.

  3. We further provide a detailed discussion in Sect. 5.5.1 and the supplementary material.

  4. Our test for the spike camera follows the procedure defined in an international standard called EMVA standard 1288 (Jähne, 2010, 2020) issued by the European Machine Vision Association.

  5. Different from the simulation, we do not take DBR and 3 S into comparison with real data, mainly due to their prohibitive time complexity (more than 10 h to reconstruct a 10-frame HSV clip).

  6. Except for the first convolution layer, which has \(n_k=32\) input channels and takes \(n_k\) spike planes at once.

  7. Please refer to the supplementary material for detailed theoretical analysis.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grants No. 62027804, No. 62425101, No. 62332002, No. 62088102, No. 62302041, and No. 62322204.

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Authors and Affiliations

  1. School of Computer Science, Peking University, Beijing, China

    Mengyue Geng, Ruiqin Xiong & Yonghong Tian

  2. School of Artificial Intelligence, Beijing Normal University, Beijing, China

    Lizhi Wang

  3. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Lin Zhu

  4. Peng Cheng Laboratory, Shenzhen, China

    Wei Zhang & Yonghong Tian

  5. School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School, Shenzhen, China

    Yonghong Tian

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Geng, M., Wang, L., Zhu, L. et al. Towards Ultra High-Speed Hyperspectral Imaging by Integrating Compressive and Neuromorphic Sampling. Int J Comput Vis 133, 1587–1610 (2025). https://doi.org/10.1007/s11263-024-02236-y

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