Abstract

Photon-counting lidar systems have difficulty reconstructing target depth images due to ambient noise. In this paper, we propose a novel way of using correlative photons and spatial correlations to reduce the false alarm probability. Experimental results show that the root mean square error of the depth image reconstructed by the proposed algorithm can be 1.68 times and 1.11 times better than that of the fast depth imaging denoising algorithm and log-matched filter estimation. The experimental results show that the proposed algorithm can effectively improve the reconstructed image of photon-counting lidar.

© 2019 Optical Society of America

Multi-depth photon-counting imaging based on polarisation modulation

Rui Liu, Xin Tian, Fang He, and Jiayi Ma
Opt. Express 29(24) 39362-39375 (2021)

Polarization prior to single-photon counting image denoising

Xin Tian, Wei Chen, Zhongyuan Wang, and Jiayi Ma
Opt. Express 29(14) 21664-21682 (2021)

Detection efficiency for underwater coaxial photon-counting lidar

Kangjian Hua, Bo Liu, Liang Fang, Huachuang Wang, Zhen Chen, and Yang Yu
Appl. Opt. 59(9) 2797-2809 (2020)

Robust 3D imaging based on regularization by denoising

Zi-Dong Liao, Zheng Lu, Jian Li, and Qin Wang
J. Opt. Soc. Am. A 39(11) 2001-2008 (2022)

A real-time noise filtering strategy for photon counting 3D imaging lidar

Zijing Zhang, Yuan Zhao, Yong Zhang, Long Wu, and Jianzhong Su
Opt. Express 21(8) 9247-9254 (2013)