Optics and Photonics Journal, 2013, 3, 83-85
doi:10.4236/opj.2013.32B021 Published Online June 2013 (http://www.scirp.org/journal/opj)
Ghost Imaging Lidar via Sparsity Constraints in Real
Mingliang Chen, Enrong Li, Wenlin Gong, Zunwang Bo, Xuyang Xu, Chengqiang Zhao,
Xia Shen, Wendong Xu, Shensheng Han
Key Laboratory for Quantum Optics, Center for Cold Atom Physics of CAS, Shanghai Institute of Optics and Fine Mechanics,
Chinese Academy of Sciences Shanghai, China
Email: sshan@mail.shcnc.ac.cn, cml2008@mail.siom.ac.cn
Received 2013
We present a series of results acquired at a 2-kilometer distance us ing our lidar system under sev eral weather cond itions,
clear, cloudy, light rain, moderately foggy, and night. The experimental results show that ghost imaging lidar via
spar-sity constraints can realize imaging in all these weather conditions.
Keywords: Ghost Imaging; Lidar; Atmosphere; Reconstruction Algorithm
1. Introduction
In recent years, thanks to the development of laser tech-
nology and laser detection techniques, several lidar tech-
niques have been developed, such as spot scanning im-
aging lidar [1], 3D imaging lidar [2-4] and synthetic ap-
erture imaging lidar [5-7]. Ghost imaging lidar via spar-
sity constraints (GISC lidar) is a newly developed imag-
ing lidar technique based on the ghost imaging principle
[8]. It gets target global information with a highly sensi-
tive bucket detector, and the image is reconstructed with
some algorithms. At present, most researches about ghost
imaging in atmosphere are theoretical [9, 10] or laboratory
experiments [11, 12]. We shall present the GISC lidar
experimental results under several weather conditions.
2. Experiments
Our experimental setup is shown in Figure 1. Pseudo-
thermal light is produced by a 532 nm laser pulse passing
through a rotatin g ground glass. The light is split into two
paths by a beam split ter, of which the light field intensity
of reference and test beam accounts respectively for 95%
and 5% of the total energy of the incident light. In our
GISC lidar system, the source plane optical field is im-
aged respectively to the reference plane and the target
plane throu gh the lenses and Lt [8, 13]. The target
reflects the test beam light back to the receiving system.
The aperture of the receiving telescope is 0.42 m with 5.0
m focal length. The photons collected by the receiving
system pass through an interference filter with 1nm half
bandwidth, then received by a photomultiplier tube (PMT).
Reference beam light passes through lens and im-
ages on a CCD detector. The intensity distribution of
light field
is reshaped into a row vector
N [14, 15], in which . After M obser-
vations, we obtain measurement matrix
MN of
M row vectors (1 )
, and from the M
signals recorded by the PMT. In our experiments,
, 200mn
. The outdoor detection dis-
tance is 2k m. The s cen e and targ et ar e shown in Figure 2.
The target with a size of 54 cm * 58 cm is a two-dimen-
sional resolution panel, with two groups of slits (three-
slit, and double-slit), whose center-to-center distances
between slits is 8 cm, 12 cm, respectively.
Ghost imaging reconstruction algorithm plays an es-
sential role in GISC lidar system. Although the linear
reconstruction algorithm was first developed [16],
nonlinear algorithms are usually used in a GISC lidar
system since they better exploits the information from
the collected data [8, 14, 15]. We developed a nonlinear
Figure 1. Experimental setup of GIL system.
Copyright © 2013 SciRes. OPJ
Figure 2. Scene and target of 2km field experiments.
reconstruction algorithm based on TV constraint and
Krush-Kuhn-Tucker conditions [17], solving the follow-
ing optimi zat i on pro gram:
max/ 2
.. 0,
st x
 
where TV is the total variation of the imag X‖‖ e
the i-th element of
, and
a constant.
The experimental scene is clearly visible in both clear
and cloudy conditions, as shown in the Figure 3(a1),
(b1). Under the condition of light rain, as shown in Fig-
ure 3(c1), the outline of the 2 km distance mountain,
where the target is situated, is still dimly visible. Whereas
in a moderate fog as shown in Figure 3(d1), the afore-
said mountain is invisible. Though traditional imaging
has no problem imaging the target in clear as well as
cloudy weather conditions, GISC lidar gets images of a
much higher signal-to-noise ratio (SNR), as shown in
Figure 3(a), (b). In light rain and moderate fog, affected
by low visibility caused b y rain drop s and fog , trad itional
imaging cannot see the target, as shown in Figure 3(c),
(d). By active illumination, spatial filtering, and highly
sensitive signal detection, GISC lidar manages to extract
the target's weak signals despite the low visibility and
high noises. The results of night experiments are shown
in Figure 3(e). Apparently, traditional imaging with
equal irradiation energy laser illumination is unable to
get the target's image, whereas GISC lidar can. Columns
Figure 3(3), 3(4) compare the images reconstructed by
GI linear reconstruction algorithm and TV nonlinear re-
construction algorithm using the same experimental data.
Again, apparently, the quality of the images recon-
structed by the TV algorithm is better than those recon-
structed by the GI algorithm.
3. Conclusions
In conclusion, GISC lidar's ability of imaging in real
Figure 3. GIL experimental results. Weather and atmosp-
here conditions: (a) clear, (b) cloudy, (c) light rain, (d)
moderately foggy, (e) night. (1) Scenes of Field Experimental,
(2) the results of traditional imaging, (3) images reconstructed
by GI, (4) images reconstructed by TV.
atmosphere is demonstrated. It features in its quality im-
aging in rainy, misty and foggy, and night conditions. It
is a new type of imaging lidar that can work in some
weather conditions where traditional imaging methods
can't work. The unique superiority of GISC lidar will
point to the development of its potential application
value in various areas.
4. Acknowledgements
Our thanks go to all the colleagues co-worked with us for
the outdoor experiments. This research is supported by
the Hi-Tech Research and Development Program of
China under Grant Project No.2011AA120101 and
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