Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging

Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter exposures result in Poisson-corrupted noisy images. While burst photography techniques help mitigate this tradeoff, conventional cameras are fundamentally limited in their sensor noise characteristics. Event cameras and single-photon avalanche diode (SPAD) sensors have emerged as promising alternatives to conventional cameras due to their desirable properties. SPADs are capable of single-photon sensitivity with microsecond temporal resolution, and event cameras can measure brightness changes up to 1 MHz with low bandwidth requirements. We show that these properties are complementary, and can help achieve low-light, high-speed image reconstruction with low bandwidth requirements. We introduce a sensor fusion framework to combine SPADs with event cameras to improve the reconstruction of high-speed, low-light scenes while reducing the high bandwidth cost associated with using every SPAD frame. Our evaluation, on both synthetic and real sensor data, demonstrates significant enhancements ($> 5$>5 dB PSNR) in reconstructing low-light scenes at high temporal resolution (100 kHz) compared to conventional cameras. Event-SPAD fusion shows great promise for real-world applications, such as robotics or medical imaging.

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Publication Details

Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2025-06-19
DOI
https://doi.org/10.1109/tpami.2025.3576698
Citations
3
Primary Topic
Advanced Optical Sensing Technologies
Type
article
Field-Weighted Citation Impact
3.58
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article

Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging

Edoardo Charbon, Siddharth Somasundaram, Davide Scaramuzza, Akshat Dave et al.
3 citations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Optical Sensing Technologies
3.58
article

Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging

Edoardo Charbon, Siddharth Somasundaram, Davide Scaramuzza, Akshat Dave, Manasi Muglikar, Ramesh Raskar
article en
3 citations

Abstract

Traditional cameras face a trade-off between low-light performance and high-speed imaging: longer exposure times to capture sufficient light results in motion blur, whereas shorter exposures result in Poisson-corrupted noisy images. While burst photography techniques help mitigate this tradeoff, conventional cameras are fundamentally limited in their sensor noise characteristics. Event cameras and single-photon avalanche diode (SPAD) sensors have emerged as promising alternatives to conventional cameras due to their desirable properties. SPADs are capable of single-photon sensitivity with microsecond temporal resolution, and event cameras can measure brightness changes up to 1 MHz with low bandwidth requirements. We show that these properties are complementary, and can help achieve low-light, high-speed image reconstruction with low bandwidth requirements. We introduce a sensor fusion framework to combine SPADs with event cameras to improve the reconstruction of high-speed, low-light scenes while reducing the high bandwidth cost associated with using every SPAD frame. Our evaluation, on both synthetic and real sensor data, demonstrates significant enhancements ($> 5$>5 dB PSNR) in reconstructing low-light scenes at high temporal resolution (100 kHz) compared to conventional cameras. Event-SPAD fusion shows great promise for real-world applications, such as robotics or medical imaging.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. 47(9)
University of Zurich (CH), IIT@MIT (US), École Polytechnique Fédérale de Lausanne (CH), Massachusetts Institute of Technology (US)
Affordable and clean energy
Openalex Percentile: Top 7%
Advanced Optical Sensing Technologies
3.58
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Event Cameras Meet SPADs for High-Speed, Low-Bandwidth Imaging — Edoardo Charbon, Siddharth Somasundaram, et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence (2025) | TGRS Research Map | TGRS