Ghost Imaging Lidar via Sparsity Constraints in Real Atmosphere

doi:10.4236/opj.2013.32B021

Paper Menu >>

Journal Menu >>

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

Atmosphere

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: [email protected], [email protected]

Received 2013

ABSTRACT

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

Nmn



MN of

M row vectors (1 )



, and from the M

signals recorded by the PMT. In our experiments,



1YM



3000M



, 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.

M. L. CHEN ET AL.

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,

YAX X

st x



 



‖‖‖‖

(1)

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

2011AA120102.

REFERENCES

[1] S. M. Spuler and S. D. Mayor, “Scanning Eye-Safe Elas-

tic Backscatter Lidar at 1.54um,” Journal of Atmospheric

and Oceanic Technology, Vol. 22, No. 6, pp. 696-703,

2005.doi:10.1175/JTECH1755.1

[2] R. M. Marino, T. Stephens, R. E. Hatch, J. L. McLaugh-

lin, J. G. Mooney and O'Brien, et al. “A Compact 3 D

Imaging Laser Radar System Using Geiger-Mode APD

Arrays-System and Measurements,” Proceedings of SPIE,

Vol. 5086, No. 1-15, 2003. doi:10.1117/12.501581

[3] Gleckler, A. D. and A. Gelbart. “Multiple-slit streak tube

imaging lidar (MS-STIL) applications,” Proceedings

SPIE, Vol. 4035, No. 266-278, 2000.

doi:10.1117/12.397800

[4] N. D. Hardy and J. H. Shapiro, “Computational Ghost

Imaging Versus Imaging Laser Radar for 3D Imaging,”

arXiv preprint arXiv:1212.3253, 2012. unpublished.

M. L. CHEN ET AL.

[5] S. M. Beck, J. R. Buck, W. F. Buell, R.P. Dickinson, D.

A. Kozlowski, N. J. Marechal, et al. “Synthetic-aperture

imaging laser radar: laboratory demonstration and signal

processing,” Applied Optics, Vol. 44, No. 35, pp.

7621-7629, 2005. doi:10.1364/AO.44.007621

[6] M. Bashkansky, R. L. Lucke, E. Funk, L. J. Rickard and J.

Reintjes, “Two-Dimensional Synthetic Aperture Imaging

in the Optical Domain,” Optics letters, Vol. 27, No. 22,

2002, pp. 1983-1985. doi:10.1364/OL.27.001983

[7] L. Liren, Z. Yu and Z. Yanan, “A Large-Aperture Syn-

Thetic Aperture Imaging Ladar Demonstrator and Its

Verification in Laboratory Space,” Acta Optica Sinica,

Vol. 31, No. 9, 2011, p. 0900112.

doi:10.3788/AOS201131 .0900112

[8] C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, et

al., “Ghost Imaging Lidar Via Sparsity Constraints,” Ap-

plied Physics Letters, Vol. 101, 2012, p. 141123.

doi:10.1063/1.4757874

[9] J. Cheng, “Ghost imaging through turbulent atmosphere,”

Optics express, Vol. 17, No.10, 2009, pp.7916-7921.

doi:10.1364/OE.17.007916

[10] P. Zhang, W. Gong, X. Shen and S. Han, “Correlated

imaging through atmospheric turbulence,” Physical Re-

view A, Vol. 82, 2010, p. 033817.

doi:10.1103/PhysRevA.82.033817

[11] R. E. Meyers, K. S. Deacon, Y. Shih, “Positive-negative

turbulence-free ghost imaging,” Applied Physics Letters,

Vol.100,No.13,2012,p.131114.

doi:10.1063/1.3698158

[12] R. E. Meyers, , K. S. Deacon, A. D. Tunic k and Y. Shih,

“Virtual Ghost Imaging through Turbulence and Obscur-

ants Using Bessel Beam Illumination,” Applied Physics

Letters,Vol.100,No.6, 2012,pp.061126-061129.

doi:10.1063/1.3684613

[13] P. Zhang, W. Gong, X. Shen, D. Huang and S. Han, “Im-

proving resolution by the second-order correlation of light

fields,” Optics letters,Vol.34, No.8, 2009,pp. 1222-1224.

doi:10.1364/OL.34.001222

[14] O. Katz, Y. Bromberg and Y. Silberberg, “Compressive

ghost imaging,” Applied Physics Letters,Vol.95, No.13,

2009,pp.131110-131112. doi:10.1063/1.3238296

[15] W. Gong and S. Han, “Super-Resolution Ghost Imaging

via Compressive Sampling Reconstruction,” arXiv pre-

print arXiv:0910.4823, 2009.

[16] W. Gong and S. Han, “A Method to Improve the Visibil-

ity of Ghost Images Obtained by Thermal Light,” Physics

Letters A, Vol. 374, No.8, 2010, pp. 1005-1008.

doi:10.1016/j.physleta.2009.12.030

[17] E. Li, M. Chen, W. Gong, H. Wang and S. Han, “A Fast

Converging Sparse Reconstruction Algorithm in Ghost

Imaging,” Journal of Electronics, No.6,2012, pp. 617-620.

doi:10.1007/s11767-012-0878-8