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Statistical hypothesis testing for dynamic scenes recovery from low-light binary photon streams
Optics Express
|August 14, 2026
Summary
This study introduces a novel statistical method for reconstructing dynamic scenes from sparse photon data captured by single-photon avalanche diode (SPAD) sensors. The approach robustly detects motion using photon arrival statistics, enabling accurate low-light imaging without explicit motion estimation.
Area of Science:
- Computer Vision
- Photonics
- Signal Processing
Background:
- Single-photon avalanche diode (SPAD) sensors offer high sensitivity for low-light imaging.
- Reconstructing dynamic scenes from sparse photon streams under photon starvation is challenging due to unstable motion estimation.
Purpose of the Study:
- To develop a robust method for dynamic scene reconstruction from binary photon streams under photon starvation.
- To overcome the limitations of explicit motion estimation in sparse data scenarios.
Main Methods:
- Introduced a statistical hypothesis-testing approach to detect motion by testing temporal stationarity of photon arrival statistics.
- Developed an iterative process integrating photon compensation and motion correction.
- Proposed three complementary evaluation metrics: mixed entropy, temporal correlation analysis, and single-pixel photon transients.
Main Results:
- Successfully reconstructed challenging dynamic scenes, including rigid-body rotation (down to 0.03 photons per pixel) and non-rigid deformation (balloon rupture at ~0.82 PPP).
- Demonstrated robustness and viability of the statistical perspective for dynamic scene recovery under ultralow light conditions.
Conclusions:
- The proposed statistical approach provides a data-efficient and training-free pathway for dynamic scene recovery.
- This method is valuable for low-light applications in biomedical imaging and industrial inspection.
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