Depth imaging denoising of photon-counting lidar

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 d...

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Bibliographic Details
Published in:Applied optics (2004) 2019-06, Vol.58 (16), p.4390
Main Authors: Huang, Pengwei, He, Weiji, Gu, Guohua, Chen, Qian
Format: Article
Language:English
Subjects:
Algorithms
False alarms
Image reconstruction
Lidar
Matched filters
Noise reduction
Photons
Target recognition
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Summary: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.
ISSN:1559-128X
2155-3165
DOI:10.1364/AO.58.004390