Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Department of Computer Science, Portland State University, Portland, OR, USA.
Nat Commun. 2023 May 31;14(1):3158. doi: 10.1038/s41467-023-38893-9.
Image sensors capable of capturing individual photons have made tremendous progress in recent years. However, this technology faces a major limitation. Because they capture scene information at the individual photon level, the raw data is sparse and noisy. Here we propose CASPI: Collaborative Photon Processing for Active Single-Photon Imaging, a technology-agnostic, application-agnostic, and training-free photon processing pipeline for emerging high-resolution single-photon cameras. By collaboratively exploiting both local and non-local correlations in the spatio-temporal photon data cubes, CASPI estimates scene properties reliably even under very challenging lighting conditions. We demonstrate the versatility of CASPI with two applications: LiDAR imaging over a wide range of photon flux levels, from a sub-photon to high ambient regimes, and live-cell autofluorescence FLIM in low photon count regimes. We envision CASPI as a basic building block of general-purpose photon processing units that will be implemented on-chip in future single-photon cameras.
近年来,能够捕获单个光子的图像传感器取得了巨大进展。然而,这项技术面临着一个主要的限制。由于它们在单个光子水平上捕获场景信息,原始数据稀疏且存在噪声。在这里,我们提出了 CASPI:主动单光子成像的协作光子处理,这是一种与技术无关、与应用无关且无需训练的光子处理管道,适用于新兴的高分辨率单光子相机。通过协作利用时空光子数据立方体中的局部和非局部相关性,即使在非常具有挑战性的照明条件下,CASPI 也能可靠地估计场景属性。我们通过两个应用演示了 CASPI 的多功能性:从亚光子到高环境光强的大范围光通量水平的 LiDAR 成像,以及低光子计数水平下的活细胞自发荧光 FLIM。我们设想 CASPI 作为通用光子处理单元的基本构建块,未来将在单光子相机的芯片上实现。