@inproceedings{1bae004e16bc476ba936fad6a56c4fa9,
title = "Depth imaging through realistic fog using Gm-APD Lidar",
abstract = "When using Gm-APD Lidar for depth imaging through realistic fog, the echo signal of the target is submerged in the background noise due to the strong absorption and scattering characteristics of the fog particles, resulting in serious defect of the recovered depth image of the target. To solve this problem, this paper proposes a dual-parameter estimation algorithm based on continuous wavelet transform (CWT) and maximum likelihood estimation (MLE) to improve the accuracy of fog signal estimation. Then the target and the fog signal are separated by estimating the fog signal of each pixel. Finally, the depth image of the separated target is processed by cross pixel complement and median filtering algorithms to improve the integrity of the target image. The experimental results show that, compared with the traditional algorithm, the target recovery of the reconstructed image is improved by 0.337, and the relative average ranging error is reduced by 0.3897. This research improves the weather adaptability of Gm-APD Lidar.",
keywords = "Depth imaging through fog, Gamma distribution, Gm-APD Lidar, Target recovery",
author = "Yinbo Zhang and Sining Li and Peng Jiang and Jianfeng Sun and Di Liu and Xianhui Yang and Xin Zhang and Hailong Zhang",
note = "Publisher Copyright: {\textcopyright} 2021 SPIE.; 16th National Conference on Laser Technology and Optoelectronics ; Conference date: 03-06-2021 Through 06-06-2021",
year = "2021",
doi = "10.1117/12.2601815",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Weibiao Chen and Jianrong Qiu and Pu Wang and Zhenxi Zhang and Jianqiang Zhu",
booktitle = "Sixteenth National Conference on Laser Technology and Optoelectronics",
address = "美国",
}