光子间隔极限下的视频成像
Inter-Photon-Limited Videography
提出神经通量场（Neural Flux Fields），将光子到达的统计建模与神经网络先验结合，用于在光子间隔极限下重建动态场景视频，为极低光照或高速视频采集提供可复用方法。
原文依据 we introduce Neural Flux Fields---a technique that combines statistical modeling of photon arrival with intrinsic priors of a neural network to achieve robust videography at the inter-photon limit.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们考虑当场景外观变化可能快于光子到达速率时，对动态场景进行成像的问题。在这种情况下，像素实际上对其检测到的光子之间时间跨度内发生的外观变化是“盲”的，因此光子间隔对视频采集系统构成了显著的速度障碍。为了分析和推进光子间隔极限下的成像能力，我们引入了一种新颖的时变通量重参数化方法，通过将通量函数的傅里叶分解与每个振荡周期内到达的光子数相关联，揭示了信号重建的内在难度。我们发现，一般场景的光子间隔极限视频成像尚未得到充分探索，且超出了现有重建技术的范围。为此，我们引入了神经通量场——一种将光子到达的统计建模与神经网络的内在先验相结合的技术，以在光子间隔极限下实现稳健的视频成像。利用该方法，我们在跨越光子间隔极限区域的一系列采集的单光子视频数据集上展示了前所未有的视频重建能力。
英文原文摘要 1216 字符
We consider the problem of imaging a dynamic scene when scene appearance variations can outpace photon arrivals. Under such conditions, a pixel is effectively "blind" to changes in appearance that occur within the timespan separating the photons it detects, and so the inter-photon interval presents a significant speed barrier to video acquisition systems. To analyze and advance imaging capabilities at the inter-photon limit, we introduce a novel reparameterization of time-varying flux that reveals the intrinsic difficulty of signal reconstruction by relating the Fourier decomposition of a flux function to the number of photons arriving within each oscillation period. We find that inter-photon-limited videography of general scenes is underexplored and beyond the reach of existing reconstruction techniques. To this end, we introduce Neural Flux Fields---a technique that combines statistical modeling of photon arrival with intrinsic priors of a neural network to achieve robust videography at the inter-photon limit. Using this approach, we demonstrate never-before-seen capabilities in video reconstruction across a range of captured single-photon video datasets spanning the inter-photon-limited regime.