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Abstract

Single photon LiDAR is considered as one of the most important tools in acquiring target information with high accuracy under extreme imaging conditions, as it offers single photon sensitivity and picosecond timing resolution. However, such technique sense the scene with the photons reflected by the target, thus resulting in severe degradation of image in presence of strong noise. Range gating with high-speed electronics is an effective way to suppress the noise, unfortunately, such technique suffers from manually selecting the parameters and limited gating width. This paper presents a target information extracting and image restoration method under large observation window, which first obtain the depth distribution of the target and extract the information within the range by analyzing the model of signal and noise, then further improve the image quality by adopting advanced image restoration algorithm and henceforth shows better results than those denoising method that purely relying on hardware. In the experiment, photon-per-pixel (PPP) was as low as 3.020 and signal-to-background ratio (SBR) was as low as 0.106, the proposed method is able to improve SBR with a factor of 19.330. Compared to classical algorithm named cross correlation, the reconstruction signal to noise ratio (RSNR) increased 33.520dB by further cooperating with advanced image restoration algorithm, thus improved the ability of sensing accurate target information under extreme cases.

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Authors and contacts

Corresponding author:Hao Wei,hwei@opt.ac.cn

Funds:Project supported by the China Postdoctoral Science Foundation (Grant No. 2020M683600), the Strategic High Technology Innovation Project of the Chinese Academy of Sciences (Grant No. GQRC-19-19), the Youth Innovation Promotion Association XIOPM-CAS, and the self determined project of Pilot national laboratory for marine science and technology (Qingdao)

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Cited By

DownLoad: Full-Size Img PowerPoint

Algorithm 1基于自适应门控的噪声抑制(AGNR)算法
1: 输入: $ \widehat{{\boldsymbol Y}} $,γ,ω,σ 2: 初始化$ T_N $,N,λ, Λ, PPP, SBR, $\varepsilon$, $ i = 1 $ 3: $\theta=\lambda \times N+(\varLambda-\lambda \times N) \times \frac{\omega}{\gamma}{\rm{e}}^{(-\frac{{\rm{SBR}}}{\gamma})}$ 4:while$ i = 1 $do 5: $ \{{\boldsymbol{\zeta}} = [T_a, T_b]\} \leftarrow (\widehat{{\boldsymbol Y}} > \theta) $ 6: $E=\|\sum_{i = T_a}^{T_b}\widehat{ {\boldsymbol Y} }-[{\rm{PPP}}+\lambda \times (T_b-T_a)] \times N\|$ 7: $T_{\rm{c} }= \Big\lceil \dfrac{E}{ \langle (\gamma/T) \times{\rm{ PPP} }, \lambda \rangle } \Big\rceil,$ 8: $(\theta_1, \theta_2) \leftarrow [(\underrightarrow{\zeta})_{T_{\rm{c}}}, (\underleftarrow{\bar{\zeta} })_{T_{\rm{c}}}]$ 9: 根据(7)式与(8)式计算$ E_1 $与$ E_2 $ 10: $ \theta=(\theta_1, \theta_2) \leftarrow \lfloor E_1, E_2 \rfloor $ 11:if$ E < \epsilon $then 12: $ i = 0 $ 13:end if 14:end while 15: $ ({\boldsymbol{\zeta}} > \gamma) \leftarrow 0 $ 16: $ {\boldsymbol{\zeta}} \leftarrow \{[{\boldsymbol{\zeta}}-\sigma] \cup [{\boldsymbol{\zeta}}+\sigma\} $

Algorithm 1基于自适应门控的噪声抑制(AGNR)算法

1: 输入: $ \widehat{{\boldsymbol Y}} $,γ,ω,σ
2: 初始化$ T_N $,N,λ, Λ, PPP, SBR, $\varepsilon$, $ i = 1 $
3: $\theta=\lambda \times N+(\varLambda-\lambda \times N) \times \frac{\omega}{\gamma}{\rm{e}}^{(-\frac{{\rm{SBR}}}{\gamma})}$
4:while$ i = 1 $do
5: $ \{{\boldsymbol{\zeta}} = [T_a, T_b]\} \leftarrow (\widehat{{\boldsymbol Y}} > \theta) $
6: $E=|\sum_{i = T_a}^{T_b}\widehat{ {\boldsymbol Y} }-[{\rm{PPP}}+\lambda \times (T_b-T_a)] \times N|$
7: $T_{\rm{c} }= \Big\lceil \dfrac{E}{ \langle (\gamma/T) \times{\rm{ PPP} }, \lambda \rangle } \Big\rceil,$
8: $(\theta_1, \theta_2) \leftarrow [(\underrightarrow{\zeta})_{T_{\rm{c}}}, (\underleftarrow{\bar{\zeta} })_{T_{\rm{c}}}]$
9: 根据(7)式与(8)式计算$ E_1 $与$ E_2 $
10: $ \theta=(\theta_1, \theta_2) \leftarrow \lfloor E_1, E_2 \rfloor $
11:if$ E < \epsilon $then
12: $ i = 0 $
13:end if
14:end while
15: $ ({\boldsymbol{\zeta}} > \gamma) \leftarrow 0 $
16: $ {\boldsymbol{\zeta}} \leftarrow \{[{\boldsymbol{\zeta}}-\sigma] \cup [{\boldsymbol{\zeta}}+\sigma\} $

DownLoad: CSV

场景	Recycle			Art			调节支座
场景	Recycle1	Recycle2	Recycle3	Art1	Art2	Art3	调节支座1	调节支座2	调节支座3
PPP	0.951	6.226	12.727	0.833	4.664	11.553	3.020	30.044	150.144
SBR	0.581	0.127	0.031	0.013	0.114	0.014	0.106	0.104	0.104
$\textrm{SBR}^{’}$	6.435	1.431	0.363	0.222	1.931	0.250	2.049	2.050	2.029
NRR	${11.076}$	${11.268}$	${11.710}$	${17.077}$	${16.939}$	${17.857}$	${19.330}$	${19.712}$	${19.510}$

DownLoad: CSV

场景	评价对象	Recycle			Art			调节支座
场景	评价对象	Recycle1	Recycle2	Recycle3	Art1	Art2	Art3	调节支座1	调节支座2	调节支座3
互相关	距离图	$-9.743$	$-8.533$	$-8.231$	$-15.597$	$-10.482$	$-10.848$	$-4.932$	$-3.023$	$-1.338$
互相关	反射率图	$-4.101$	$-0.908$	$-3.691$	$-21.453$	$-3.504$	$-8.024$	$4.533$	$4.687$	$4.698$
AGCC	距离图	$11.524$	$17.961$	$17.577$	$12.862$	$19.789$	$19.633$	$22.352$	$24.751$	$26.369$
AGCC	反射率图	$0.114$	$6.433$	$6.062$	$-7.806$	$4.175$	$3.457$	$5.324$	$7.504$	$7.731$
中值滤波	距离图	$13.954$	${22.304}$	${21.881}$	$18.552$	$27.340$	$26.903$	$24.389$	$25.909$	$26.788 $
中值滤波	反射率图	$5.844$	$12.608$	$12.284$	$2.316$	${10.584}$	$10.261$	$8.383$	$9.500$	$9.571$
UA	距离图	${20.768}$	$21.318$	$21.321$	$20.519$	$24.761$	$24.764$	${28.588}$	${28.741}$	${29.199}$
UA	反射率图	$3.304$	${16.755}$	${18.760}$	$5.513$	$10.034$	${11.122}$	${7.926}$	${9.677}$	${9.890}$
OPN3DR	距离图	$20.075$	$21.430$	$21.172$	${22.767}$	${27.453}$	${27.397}$	$25.726$	$26.801$	$28.298$
OPN3DR	反射率图	${11.093}$	$15.812$	$15.380$	${8.910}$	$10.448$	$10.255$	$7.915$	$8.979$	$9.004$

DownLoad: CSV

场景	Recycle			Art			调节支座
场景	${\rm{Recycle1}}$	${\rm{Recycle2}}$	${\rm{ Recycle3}}$	${\rm{Art1}}$	${\rm{ Art2 }}$	${\rm{Art3}}$	调节支座1	调节支座2	调节支座3
互相关	0.679	0.690	0.711	2.663	2.693	2.720	0.273	0.260	0.286
AGCC	0.085	0.085	0.085	0.220	0.224	0.218	0.023	0.024	0.024
中值滤波	0.095	0.096	0.096	0.235	0.239	0.234	0.035	0.035	0.036
UA	6.290	7.759	10.787	20.335	13.517	19.832	4.320	5.073	8.711
OPN3DR	1.422	1.344	1.986	5.464	5.518	5.228	0.145	0.228	0.724

DownLoad: CSV

场景	评价对象	距离图RSNR	反射率图RSNR	运行时间/s
调节支座1	多尺度滤波	${27.441}$	$0.125$	$0.785$
调节支座1	最大尺度滤波	$25.726$	${7.915}$	${0.145}$
调节支座2	多尺度滤波	${29.273}$	$1.218$	$0.772$
调节支座2	最大尺度滤波	$26.801$	${8.979}$	${0.228}$
调节支座3	多尺度滤波	${30.552}$	$7.030$	$0.781$
调节支座3	最大尺度滤波	$28.298$	${9.004}$	${0.724}$

DownLoad: CSV

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Vol. 71, Issue 10 - 2022-05-20

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