Dr. Fanghua Liu Profile

Dr. Fanghua Liu

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Proceedings Article | 1 November 2021 Paper

Image-based noise filtering method for Geiger-mode APD array Lidar data

Yongqiang Chen, Yan He, Yuan Luo, Fanghua Liu, Chongmiao Jiao, Shouchuan Guo, Yifan Huang, Weibiao Chen

Proceedings Volume 12057, 120573B (2021) https://doi.org/10.1117/12.2606406

KEYWORDS: Image filtering, LIDAR, Imaging systems, 3D image processing, Target detection, Image processing, Data processing, 3D acquisition

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Single-photon counting Lidar (SPL) systems using Geiger mode Avalanche Photo Diode (Gm-APD) arrays are more sensitive than traditional linear mode Lidar systems and are capable to detect the sparse target-returned photon less than single photon. However the single-photon sensitivity of SPL system also make it susceptible to solar background light which is a major noise source of SPL data. The relatively high noise level of SPL systems poses a significant challenges to the noise filter processing of measured data. In this paper, two image based noise filtering methods: K-Nearest- Neighbor (KNN) filtering method and Single Frame Histogram (SFH) filtering method were proposed, to reduce the noise points in Gm-APD array Lidar data. In these methods, noise points were removed through raw image data processing. We count the number of corresponding time of flight data points in single frame image and remove the noise points from signal through a predefined threshold. The noise filtering results of the two proposed methods were analyzed and compared based on raw data obtained from our 64×64 Gm-APD array Lidar imaging experiment. The noise filtered image results show that more than 90% of the noise points in single frame data has been removed. Finally, the noise filtered image data was further processed to get the cleaned 3D images. The results indicates that the proposed imagebase noise filtering methods is suitable for the noise reduction processing of our (Gm-APD) array Lidar data.

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