Multi-channel high-precision FPGA-embedded time correlated single photon counting systems for optical scattering imaging

Pei, Chengquan and Li, Haoyong and Zeng, Xuekang and Wang, Yu and Chen, Haochang and Li, David (2025) Multi-channel high-precision FPGA-embedded time correlated single photon counting systems for optical scattering imaging. Optics Express, 33 (22). pp. 46626-46641. ISSN 1094-4087 (https://doi.org/10.1364/OE.568299)

[thumbnail of Pei-etal-2025-OE-Multi-channel-high-precision-FPGA-embedded-time-correlated]
Preview
Text. Filename: Pei-etal-2025-OE-Multi-channel-high-precision-FPGA-embedded-time-correlated.pdf
Final Published Version
License: Other

Download (3MB)| Preview

Abstract

Scattering imaging techniques, demonstrated using time-correlated single-photon counting (TCSPC) systems, have significant industrial applications in areas such as remote sensing, robotic vision, and autonomous driving. This paper presents a multi-channel, high-linearity TCSPC system based on a Xilinx UltraScale (XC7Z100-2FFG9001) field-programmable gate array (FPGA). The proposed TCSPC system achieves a temporal resolution of 10 ps and exhibits differential nonlinearity within the range of [-10, 9.9] ps and integral nonlinearity within [-14, 9.9] ps. To demonstrate the capabilities of the proposed TCSPC system, we conducted confocal imaging experiments to visualise objects obscured by scattering media. We introduce what we believe to be a novel image reconstruction method based on the plug-and-play (PnP) framework, which leverages fast and flexible denoising neural networks: FFDNet for 2D imaging and FastDVDnet for 3D imaging. By integrating the scattering forward model with these denoising networks, we implement a high-performance reconstruction approach that enables high-quality imaging of complex scenes. Both simulations and experiments were conducted to validate the superior performance of our proposed method.

ORCID iDs

Pei, Chengquan, Li, Haoyong, Zeng, Xuekang, Wang, Yu, Chen, Haochang and Li, David ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;