单光子视频成像的时空通量探测
Spatiotemporal Flux Probing for Single-Photon Videography
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中文总结 AI 辅助
针对极端光子稀疏下高速视频恢复难题，提出时空通量探测理论，直接从光子流估计傅里叶系数，以更少光子恢复快速运动，支持速度选择性成像并泛化多种相机。
中文摘要 AI 辅助
我们解决了在极端光子稀疏条件下从动态场景恢复高速视频的问题。现有方法依赖于在局部时空窗口中聚合光子探测以提高信噪比；然而，这种局部分组丢弃了全局结构，并且在光子探测在空间和时间上稀疏的低光照条件下失效。在这项工作中，我们表明，恢复运动和光照所需的信息编码在光子到达的完整时空模式的相关性中。基于这一见解，我们发展了一种时空通量探测理论和一种算法，该算法直接从光子流中估计底层强度的傅里叶系数。我们证明，我们的方法（1）以比先前方法少得多的光子恢复快速运动和时间光照动态，（2）实现速度选择性视频成像，自动将视频重新聚焦到特定检测到的运动上，以及（3）跨传感模态泛化，包括单光子、事件和尖峰相机。
英文摘要
We address the problem of recovering high-speed videos from dynamic scenes under extreme photon sparsity. Existing methods rely on aggregating photon detections in local spatiotemporal windows to improve signal-to-noise ratio; however, this local grouping discards global structure and fails in low-light regimes where photon detections are sparse in space and time. In this work, we show that the information needed to recover both motion and illumination is encoded in correlations over the full space-time pattern of photon arrivals. Building on this insight, we develop a spatiotemporal flux probing theory and an algorithm that estimates the Fourier coefficients of the underlying intensity directly from the photon stream. We demonstrate that our approach (1) recovers fast motion and temporal illumination dynamics with substantially fewer photons than prior methods, (2) enables velocity-selective videography that automatically refocuses video onto specific detected motions, and (3) generalizes across sensing modalities including single-photon, event, and spike cameras.
发表机构
- Purdue University（普渡大学）
- University of Toronto（多伦多大学）
- Massachusetts Institute of Technology（麻省理工学院）
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