Abstract

Ghost imaging through inhomogeneous turbulent atmosphere along an uplink path and a downlink path is studied in detail by using the numerical simulation method. Based on the Hufnagel-Valley5/7 turbulent atmosphere profile model, the numerical imaging formula of ghost imaging through turbulent atmosphere along a slant path is derived and used to analyze the influence of turbulent atmosphere along an uplink path and a downlink path on the imaging quality, and the effect from the zenith angle is also discussed. The numerical results show that the imaging quality through turbulent atmosphere along a downlink path is better than that along an uplink one, which can be explained by the phase modulation effect.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (6)

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
[Crossref]

F. Wang, H. Wang, H. C. Wang, G. W. Li, and G. H. Situ, “Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging,” Opt. Express 27(18), 25560–25572 (2019).
[Crossref]

W. Tan, X. W. Huang, S. Q. Nan, Y. F. Bai, and X. Q. Fu, “Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere,” J. Opt. Soc. Am. A 36(7), 1261–1266 (2019).
[Crossref]

2018 (9)

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

X. L. Liu, F. Wang, M. H. Zhang, and Y. J. Cai, “Effects of atmospheric turbulence on lensless ghost imaging with partially coherent light,” Appl. Sci. 8(9), 1479 (2018).
[Crossref]

J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
[Crossref]

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
[Crossref]

S. Chen, “X-ray ghost images could cut radiation doses,” Science 359(6383), 1452 (2018).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
[Crossref]

2017 (3)

H. C. Liu and S. Zhang, “Computational ghost imaging of hot objects in long-wave infrared range,” Appl. Phys. Lett. 111(3), 031110 (2017).
[Crossref]

M. Lyu, W. Wang, H. Wang, H. C. Wang, G. W. Li, N. Chen, and G. H. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
[Crossref]

X. H. Shi, H. X. Li, Y. F. Bai, and X. Q. Fu, “Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels,” Appl. Opt. 56(26), 7320–7326 (2017).
[Crossref]

2016 (1)

D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
[Crossref]

2015 (5)

W. L. Gong, “High-resolution pseudo-inverse ghost imaging,” Photonics Res. 3(5), 234–237 (2015).
[Crossref]

H. Ghanbari-Ghalehjoughi, S. Ahmadi-Kandjani, and M. Eslami, “High quality computational ghost imaging using multi-fluorescent screen,” J. Opt. Soc. Am. A 32(2), 323–328 (2015).
[Crossref]

Y. K. Xu, W. T. Liu, E. F. Zhang, Q. Li, H. Y. Dai, and P. X. Chen, “Is ghost imaging intrinsically more powerful against scattering,” Opt. Express 23(26), 32993–33000 (2015).
[Crossref]

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
[Crossref]

2014 (3)

2013 (5)

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

X. Wang and Y. X. Zhang, “Lens ghost imaging in a non-Kolmogorov slant turbulence atmosphere,” Optik 124(20), 4378–4382 (2013).
[Crossref]

J. Chen and J. Lin, “Unified theory of thermal ghost imaging and ghost diffraction through turbulent atmosphere,” Phys. Rev. A 87(4), 043810 (2013).
[Crossref]

S. M. Zhao, B. Wang, L. Y. Gong, Y. B. Sheng, W. W. Cheng, X. L. Dong, and B. Y. Zheng, “Improving the atmosphere turbulence tolerance in holographic ghost imaging system by channel coding,” J. Lightwave Technol. 31(17), 2823–2828 (2013).
[Crossref]

2012 (5)

S. M. Zhao, J. Leach, L. Y. Gong, J. Ding, and B. Y. Zheng, “Aberration corrections for free-space optical communications in atmosphere turbulence using orbital angular momentum states,” Opt. Express 20(1), 452–461 (2012).
[Crossref]

B. I. Erkmen, “Computational ghost imaging for remote sensing,” J. Opt. Soc. Am. A 29(5), 782–789 (2012).
[Crossref]

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

R. E. Meyers, K. S. Deacon, and Y. H. Shih, “Positive-negative turbulence-free ghost imaging,” Appl. Phys. Lett. 100(13), 131114 (2012).
[Crossref]

Y. X. Zhang and Y. G. Wang, “Computational lensless ghost imaging in a slant path non-Kolmogorov turbulent atmosphere,” Optik 123(15), 1360–1363 (2012).
[Crossref]

2011 (3)

N. D. Hardy and J. H. Shapiro, “Reflective ghost imaging through turbulence,” Phys. Rev. A 84(6), 063824 (2011).
[Crossref]

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

2010 (2)

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential Ghost Imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
[Crossref]

2009 (2)

2008 (1)

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78(6), 061802 (2008).
[Crossref]

2007 (1)

Y. F. Bai and S. S. Han, “Ghost imaging with thermal light by third-order correlation,” Phys. Rev. A 76(4), 043828 (2007).
[Crossref]

2004 (1)

E. Gatti, M. Brambilla, L. A. Bache, and Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

1995 (2)

D. V. Strekalov, A. V. Sergienko, D. N. Klyshko, and Y. H. Shih, “Observation of two-photon ‘ghost’ interference and diffraction,” Phys. Rev. Lett. 74(18), 3600–3603 (1995).
[Crossref]

T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
[Crossref]

1988 (1)

1979 (1)

T. Aulin, “A modified model for the fading signal at a mobile radio channel,” IEEE Trans. Veh. Technol. 28(3), 182–203 (1979).
[Crossref]

Ahmadi-Kandjani, S.

An, J. Y.

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Andrews, L. C.

L. C. Andrews and R. L. Phillips, Laser Beam Propagation through Random Media (SPIE, 1998).

Aulin, T.

T. Aulin, “A modified model for the fading signal at a mobile radio channel,” IEEE Trans. Veh. Technol. 28(3), 182–203 (1979).
[Crossref]

Bache, L. A.

E. Gatti, M. Brambilla, L. A. Bache, and Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Bai, Y. F.

W. Tan, X. W. Huang, S. Q. Nan, Y. F. Bai, and X. Q. Fu, “Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere,” J. Opt. Soc. Am. A 36(7), 1261–1266 (2019).
[Crossref]

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

X. H. Shi, H. X. Li, Y. F. Bai, and X. Q. Fu, “Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels,” Appl. Opt. 56(26), 7320–7326 (2017).
[Crossref]

Y. F. Bai and S. S. Han, “Ghost imaging with thermal light by third-order correlation,” Phys. Rev. A 76(4), 043828 (2007).
[Crossref]

Bhuiyan, M. Z. A.

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Bilal, K.

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

Bo, Z.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Boyd, R. W.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

K. W. C. Chan, M. N. O’Sullivan, and R. W. Boyd, “High-order thermal ghost imaging,” Opt. Lett. 34(21), 3343–3345 (2009).
[Crossref]

Brambilla, M.

E. Gatti, M. Brambilla, L. A. Bache, and Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Cai, J.

J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
[Crossref]

Cai, Y. J.

X. L. Liu, F. Wang, M. H. Zhang, and Y. J. Cai, “Effects of atmospheric turbulence on lensless ghost imaging with partially coherent light,” Appl. Sci. 8(9), 1479 (2018).
[Crossref]

X. L. Liu, F. Wang, C. Wei, and Y. J. Cai, “Experimental study of turbulence-induced beam wander and deformation of a partially coherent beam,” Opt. Lett. 39(11), 3336–3339 (2014).
[Crossref]

Cantelli, V.

D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
[Crossref]

Cao, J. S.

Chan, K. W. C.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

K. W. C. Chan, M. N. O’Sullivan, and R. W. Boyd, “High-order thermal ghost imaging,” Opt. Lett. 34(21), 3343–3345 (2009).
[Crossref]

Chen, C.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Chen, H.

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
[Crossref]

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

Chen, J.

J. Chen and J. Lin, “Unified theory of thermal ghost imaging and ghost diffraction through turbulent atmosphere,” Phys. Rev. A 87(4), 043810 (2013).
[Crossref]

Chen, J. G.

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

Chen, M.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Chen, M. L.

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

Chen, N.

M. Lyu, W. Wang, H. Wang, H. C. Wang, G. W. Li, N. Chen, and G. H. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
[Crossref]

Chen, P. X.

Chen, S.

S. Chen, “X-ray ghost images could cut radiation doses,” Science 359(6383), 1452 (2018).
[Crossref]

Chen, W. H.

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Chen, Z. Y.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Cheng, J.

Cheng, W. W.

Dai, H. Y.

Deacon, K. S.

R. E. Meyers, K. S. Deacon, and Y. H. Shih, “Positive-negative turbulence-free ghost imaging,” Appl. Phys. Lett. 100(13), 131114 (2012).
[Crossref]

Ding, J.

Dixon, P. B.

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

Dong, F.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

Dong, G. X.

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
[Crossref]

Dong, X. L.

Duan, C.

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

Eberly, J. H.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

Erkmen, B. I.

Eslami, M.

Fan, H.

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

Ferri, F.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential Ghost Imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

Flatté, S. M.

Fu, L.

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Fu, X. Q.

W. Tan, X. W. Huang, S. Q. Nan, Y. F. Bai, and X. Q. Fu, “Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere,” J. Opt. Soc. Am. A 36(7), 1261–1266 (2019).
[Crossref]

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

X. H. Shi, H. X. Li, Y. F. Bai, and X. Q. Fu, “Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels,” Appl. Opt. 56(26), 7320–7326 (2017).
[Crossref]

Gao, F. L.

Gatti, A.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential Ghost Imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

Gatti, E.

E. Gatti, M. Brambilla, L. A. Bache, and Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Gbur, G.

Ghanbari-Ghalehjoughi, H.

Gong, L. Y.

Gong, W.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
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Gong, W. L.

W. L. Gong, “High-resolution pseudo-inverse ghost imaging,” Photonics Res. 3(5), 234–237 (2015).
[Crossref]

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
[Crossref]

Guan, J.

Guo, S. X.

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
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C. Zhang, S. X. Guo, J. S. Cao, J. Guan, and F. L. Gao, “Object reconstitution using pseudo-inverse for ghost imaging,” Opt. Express 22(24), 30063–30073 (2014).
[Crossref]

Han, S. S.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
[Crossref]

Y. F. Bai and S. S. Han, “Ghost imaging with thermal light by third-order correlation,” Phys. Rev. A 76(4), 043828 (2007).
[Crossref]

Hardy, N. D.

N. D. Hardy and J. H. Shapiro, “Reflective ghost imaging through turbulence,” Phys. Rev. A 84(6), 063824 (2011).
[Crossref]

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

He, Y. C.

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
[Crossref]

Howell, J. C.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

Howland, G. A.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

Hu, M.

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

Hu, Y. K.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Huang, H. Y.

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
[Crossref]

Huang, X. W.

W. Tan, X. W. Huang, S. Q. Nan, Y. F. Bai, and X. Q. Fu, “Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere,” J. Opt. Soc. Am. A 36(7), 1261–1266 (2019).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

Ji, X. L.

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

Jia, X. X.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
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D. V. Strekalov, A. V. Sergienko, D. N. Klyshko, and Y. H. Shih, “Observation of two-photon ‘ghost’ interference and diffraction,” Phys. Rev. Lett. 74(18), 3600–3603 (1995).
[Crossref]

Leach, J.

Lei, P.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

Li, E.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Li, E. R.

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

Li, G. W.

Li, H. X.

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

X. H. Shi, H. X. Li, Y. F. Bai, and X. Q. Fu, “Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels,” Appl. Opt. 56(26), 7320–7326 (2017).
[Crossref]

Li, K. L.

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Li, K. Q.

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Li, M. F.

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

Li, Q.

Li, R. F.

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

Li, X. Q.

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

Li, Z. L.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

Lin, J.

J. Chen and J. Lin, “Unified theory of thermal ghost imaging and ghost diffraction through turbulent atmosphere,” Phys. Rev. A 87(4), 043810 (2013).
[Crossref]

Liu, D. Q.

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
[Crossref]

Liu, H. C.

H. C. Liu and S. Zhang, “Computational ghost imaging of hot objects in long-wave infrared range,” Appl. Phys. Lett. 111(3), 031110 (2017).
[Crossref]

Liu, J.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Liu, W. T.

Liu, X. L.

X. L. Liu, F. Wang, M. H. Zhang, and Y. J. Cai, “Effects of atmospheric turbulence on lensless ghost imaging with partially coherent light,” Appl. Sci. 8(9), 1479 (2018).
[Crossref]

X. L. Liu, F. Wang, C. Wei, and Y. J. Cai, “Experimental study of turbulence-induced beam wander and deformation of a partially coherent beam,” Opt. Lett. 39(11), 3336–3339 (2014).
[Crossref]

Liu, Y. H.

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
[Crossref]

Liu, Z. M.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

Lugiato,

E. Gatti, M. Brambilla, L. A. Bache, and Lugiato, “Ghost imaging with thermal light: comparing entanglement and classical correlation,” Phys. Rev. Lett. 93(9), 093602 (2004).
[Crossref]

Lugiato, L. A.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential Ghost Imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

Luo, C. L.

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

Luo, J. W.

J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
[Crossref]

Luo, K. H.

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

Lyu, M.

M. Lyu, W. Wang, H. Wang, H. C. Wang, G. W. Li, N. Chen, and G. H. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
[Crossref]

Magatti, D.

F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential Ghost Imaging,” Phys. Rev. Lett. 104(25), 253603 (2010).
[Crossref]

Martin, J. M.

Meyers, R. E.

R. E. Meyers, K. S. Deacon, and Y. H. Shih, “Positive-negative turbulence-free ghost imaging,” Appl. Phys. Lett. 100(13), 131114 (2012).
[Crossref]

Nan, S. Q.

W. Tan, X. W. Huang, S. Q. Nan, Y. F. Bai, and X. Q. Fu, “Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere,” J. Opt. Soc. Am. A 36(7), 1261–1266 (2019).
[Crossref]

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

O’Sullivan, M. N.

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

K. W. C. Chan, M. N. O’Sullivan, and R. W. Boyd, “High-order thermal ghost imaging,” Opt. Lett. 34(21), 3343–3345 (2009).
[Crossref]

O’Sullivan-Hale, C.

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

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D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
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C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
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D. Pelliccia, A. Rack, M. Scheel, V. Cantelli, and D. M. Paganin, “Experimental x-ray ghost imaging,” Phys. Rev. Lett. 117(11), 113902 (2016).
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K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
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L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
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M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
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P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
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Shi, X. H.

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
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R. E. Meyers, K. S. Deacon, and Y. H. Shih, “Positive-negative turbulence-free ghost imaging,” Appl. Phys. Lett. 100(13), 131114 (2012).
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D. V. Strekalov, A. V. Sergienko, D. N. Klyshko, and Y. H. Shih, “Observation of two-photon ‘ghost’ interference and diffraction,” Phys. Rev. Lett. 74(18), 3600–3603 (1995).
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K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
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Song, L. J.

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
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T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
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Tang, L. L.

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
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H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
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Wang, F.

Wang, G.

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Wang, H. C.

Wang, K.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
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Wang, Q.

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
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J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
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Wang, T.

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
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M. Lyu, W. Wang, H. Wang, H. C. Wang, G. W. Li, N. Chen, and G. H. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
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X. Wang and Y. X. Zhang, “Lens ghost imaging in a non-Kolmogorov slant turbulence atmosphere,” Optik 124(20), 4378–4382 (2013).
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Wang, Y. G.

Y. X. Zhang and Y. G. Wang, “Computational lensless ghost imaging in a slant path non-Kolmogorov turbulent atmosphere,” Optik 123(15), 1360–1363 (2012).
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Wei, C.

Wu, L. A.

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
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X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
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M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
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Xu, W. D.

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

Xu, X.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Xu, Y. K.

Xu, Z.

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
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X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
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J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
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X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
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J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
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J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
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Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
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Zhang, C.

Zhang, E. F.

Zhang, H.

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
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Zhang, M. H.

X. L. Liu, F. Wang, M. H. Zhang, and Y. J. Cai, “Effects of atmospheric turbulence on lensless ghost imaging with partially coherent light,” Appl. Sci. 8(9), 1479 (2018).
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Zhang, P. L.

P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
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Zhang, S.

H. C. Liu and S. Zhang, “Computational ghost imaging of hot objects in long-wave infrared range,” Appl. Phys. Lett. 111(3), 031110 (2017).
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Zhang, T.

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
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Zhang, Y.

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
[Crossref]

Zhang, Y. R.

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

Zhang, Y. X.

X. Wang and Y. X. Zhang, “Lens ghost imaging in a non-Kolmogorov slant turbulence atmosphere,” Optik 124(20), 4378–4382 (2013).
[Crossref]

Y. X. Zhang and Y. G. Wang, “Computational lensless ghost imaging in a slant path non-Kolmogorov turbulent atmosphere,” Optik 123(15), 1360–1363 (2012).
[Crossref]

Zhao, C.

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Zhao, C. Q.

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

Zhao, Q.

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

Zhao, S. M.

Zhao, Y.

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
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Zheng, B. Y.

Zhong, F.

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
[Crossref]

Zhou, C.

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
[Crossref]

Zhou, L. Q.

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Zhou, X.

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

Zhou, Y.

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
[Crossref]

Zhu, S. T.

Y. C. He, G. Wang, G. X. Dong, S. T. Zhu, H. Chen, A. X. Zhang, and Z. Xu, “Ghost imaging based on deep learning,” Sci. Rep. 8(1), 6469 (2018).
[Crossref]

AIP Adv. (1)

Y. Zhou, T. Zhang, F. Zhong, and S. X. Guo, “Enhancing image quality of ghost imaging by fuzzy c-means clustering method,” AIP Adv. 9(7), 075006 (2019).
[Crossref]

Appl. Opt. (2)

Appl. Phys. Lett. (3)

C. Q. Zhao, W. L. Gong, M. L. Chen, E. R. Li, H. Wang, W. D. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101(14), 141123 (2012).
[Crossref]

H. C. Liu and S. Zhang, “Computational ghost imaging of hot objects in long-wave infrared range,” Appl. Phys. Lett. 111(3), 031110 (2017).
[Crossref]

R. E. Meyers, K. S. Deacon, and Y. H. Shih, “Positive-negative turbulence-free ghost imaging,” Appl. Phys. Lett. 100(13), 131114 (2012).
[Crossref]

Appl. Sci. (1)

X. L. Liu, F. Wang, M. H. Zhang, and Y. J. Cai, “Effects of atmospheric turbulence on lensless ghost imaging with partially coherent light,” Appl. Sci. 8(9), 1479 (2018).
[Crossref]

Chin. Phys. B (1)

X. Yang, Y. Zhang, L. Xu, C. H. Yang, Q. Wang, Y. H. Liu, and Y. Zhao, “Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method,” Chin. Phys. B 24(12), 124202 (2015).
[Crossref]

Futur. Gener. Comp. Syst. (1)

W. H. Chen, J. Y. An, R. F. Li, L. Fu, G. Q. Xie, M. Z. A. Bhuiyan, and K. Q. Li, “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Futur. Gener. Comp. Syst. 89, 78–88 (2018).
[Crossref]

IEEE Access (1)

J. Y. An, L. Fu, M. Hu, W. H. Chen, and J. W. Zhan, “A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information,” IEEE Access 7, 20708–20722 (2019).
[Crossref]

IEEE Trans. Parallel Distrib. Syst. (1)

J. G. Chen, K. L. Li, K. Bilal, X. Zhou, K. Q. Li, and P. S. Yu, “A bi-layered parallel training architecture for large-scale convolutional neural networks,” IEEE Trans. Parallel Distrib. Syst. 30(5), 965–976 (2019).
[Crossref]

IEEE Trans. Veh. Technol. (1)

T. Aulin, “A modified model for the fading signal at a mobile radio channel,” IEEE Trans. Veh. Technol. 28(3), 182–203 (1979).
[Crossref]

J. Lightwave Technol. (1)

J. Opt. Soc. Am. A (4)

Laser Phys. (1)

L. L. Tang, Y. F. Bai, C. Duan, S. Q. Nan, Q. Shen, and X. Q. Fu, “Effects of incident angles on reflective ghost imaging through atmospheric turbulence,” Laser Phys. 28(1), 015201 (2018).
[Crossref]

Laser Phys. Lett. (2)

C. L. Luo, P. Lei, Z. L. Li, J. Q. Qi, X. X. Jia, F. Dong, and Z. M. Liu, “Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence,” Laser Phys. Lett. 15(8), 085201 (2018).
[Crossref]

X. H. Shi, X. W. Huang, S. Q. Nan, H. X. Li, Y. F. Bai, and X. Q. Fu, “Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method,” Laser Phys. Lett. 15(4), 045204 (2018).
[Crossref]

Neurocomputing (2)

J. Cai, J. W. Luo, S. L. Wang, and S. Yang, “Feature selection in machine learning: A new perspective,” Neurocomputing 300, 70–79 (2018).
[Crossref]

K. Wang, K. L. Li, L. Q. Zhou, Y. K. Hu, Z. Y. Chen, J. Liu, and C. Chen, “Multiple convolutional neural networks for multivariate time series prediction,” Neurocomputing 360, 107–119 (2019).
[Crossref]

Opt. Commun. (1)

H. Y. Huang, C. Zhou, T. Tian, D. Q. Liu, and L. J. Song, “High-quality compressive ghost imaging,” Opt. Commun. 412(1), 60–65 (2018).
[Crossref]

Opt. Express (5)

Opt. Laser Technol. (1)

H. Chen, X. L. Ji, X. Q. Li, T. Wang, Q. Zhao, and H. Zhang, “Energy focus ability of annular beams propagating through atmospheric turbulence along a slanted path,” Opt. Laser Technol. 71, 22–28 (2015).
[Crossref]

Opt. Lett. (2)

Opt. Photonics J. (1)

M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, X. Shen, W. Xu, and S. S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photonics J. 03(02), 83–85 (2013).
[Crossref]

Optik (2)

Y. X. Zhang and Y. G. Wang, “Computational lensless ghost imaging in a slant path non-Kolmogorov turbulent atmosphere,” Optik 123(15), 1360–1363 (2012).
[Crossref]

X. Wang and Y. X. Zhang, “Lens ghost imaging in a non-Kolmogorov slant turbulence atmosphere,” Optik 124(20), 4378–4382 (2013).
[Crossref]

Photonics Res. (1)

W. L. Gong, “High-resolution pseudo-inverse ghost imaging,” Photonics Res. 3(5), 234–237 (2015).
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Phys. Rev. A (9)

T. B. Pittman, Y. H. Shih, D. V. Strekalov, and A. V. Sergienko, “Optical imaging by means of two-photon quantum entanglement,” Phys. Rev. A 52(5), R3429–R3432 (1995).
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Y. F. Bai and S. S. Han, “Ghost imaging with thermal light by third-order correlation,” Phys. Rev. A 76(4), 043828 (2007).
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J. Chen and J. Lin, “Unified theory of thermal ghost imaging and ghost diffraction through turbulent atmosphere,” Phys. Rev. A 87(4), 043810 (2013).
[Crossref]

P. L. Zhang, W. L. Gong, X. Shen, and S. S. Han, “Correlated imaging through atmospheric turbulence,” Phys. Rev. A 82(3), 033817 (2010).
[Crossref]

N. D. Hardy and J. H. Shapiro, “Reflective ghost imaging through turbulence,” Phys. Rev. A 84(6), 063824 (2011).
[Crossref]

K. W. C. Chan, D. S. Simon, A. V. Sergienko, N. D. Hardy, J. H. Shapiro, P. B. Dixon, G. A. Howland, J. C. Howell, J. H. Eberly, M. N. O’Sullivan, B. Rodenburg, and R. W. Boyd, “Theoretical analysis of quantum ghost imaging through turbulence,” Phys. Rev. A 84(4), 043807 (2011).
[Crossref]

P. B. Dixon, G. A. Howland, K. W. C. Chan, C. O’Sullivan-Hale, B. Rodenburg, N. D. Hardy, J. H. Shapiro, D. S. Simon, A. V. Sergienko, R. W. Boyd, and J. C. Howell, “Quantum ghost imaging through turbulence,” Phys. Rev. A 83(5), 051803 (2011).
[Crossref]

M. F. Li, Y. R. Zhang, K. H. Luo, L. A. Wu, and H. Fan, “Time-correspondence differential ghost imaging,” Phys. Rev. A 87(3), 033813 (2013).
[Crossref]

J. H. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78(6), 061802 (2008).
[Crossref]

Phys. Rev. Lett. (4)

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[Crossref]

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Figures (6)

Fig. 1.
Fig. 1. Setup of a lensless GI system with turbulent atmosphere along an uplink path (a) and a downlink path (b).
Fig. 2.
Fig. 2. (a) is the initial collimating Gaussian beam. (b)–(d) are the optical field distributions after propagating through 1 km under free space, vertical uplink path, and vertical downlink path, respectively.
Fig. 3.
Fig. 3. (a)–(d) are the phase screen distribution, the beam wander, scintillation index, and mean phase fluctuation, respectively.
Fig. 4.
Fig. 4. (a) is the imaging object. (b)–(d) are the corresponding ghost-images under free space, vertical uplink path, and vertical downlink path, respectively.
Fig. 5.
Fig. 5. Retrieved ghost images at an uplink path (a)–(c) and a downlink (d)–(f) path under zenith angles θ = ${15^ \circ }$ , ${45^ \circ }$ and ${75^ \circ }$ , respectively.
Fig. 6.
Fig. 6. (a) CNR and (b) V of the retrieved ghost images at different zenith angles.

Equations (17)

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G ( x , y )  =  1 N n  =  1 N I r n ( x , y ) I t n 1 N n  =  1 N I r n ( x , y ) 1 N n  =  1 N I t n ,
2 i k A z + 2 A + 2 k 2 Δ n A = 0 ,
A ( x , y , z j ) = exp ( i 2 k z j 1 z j 2 d z ) × exp [ i S ( x , y , z j ) ] A ( x , y , z j 1 ) ,
A ( x , y , z )  =  j = 1 P exp ( i 2 k z j 1 z j 2 d z ) exp [ i S ( x , y , z j ) ] A ( x , y , z 0 ) ,
A ( x , y , z j ) = F  -  1 { F { exp [ i S ( x , y , z j ) ] A ( x , y , z j 1 ) } exp ( i K x 2 + K y 2 2 k Δ z j ) } ,
Φ ϕ ( κ ) = 2 π k 2 Δ z × 0.033 C n 2 ( h ) exp [ ( κ / κ m ) 2 ] ( κ 2 + κ 0 2 ) 11 / 6 ,
C n 2 ( h ) = 5.94 × 10 53 ( v / 27 ) 2 h 10 exp ( h / 1000 ) + 2.7 × 10 16 exp ( h / 1500 ) + C 0 exp ( h / 100 ) ,
C n 2 ( z , θ ) = 5.94 × 10 53 ( v / 27 ) 2 ( z cos θ + h 0 ) 10 exp ( ( z cos θ + h 0 ) / 1000 ) + 2.7 × 10 16 exp ( ( z cos θ + h 0 ) / 1500 ) + C 0 exp ( ( z cos θ + h 0 ) / 100 ) ,
C n 2 ( Δ z j )  =  1 Δ z j z j 1 z j C n 2 ( z , θ ) d z .
β 0 2  = 1 .23 C n 2 ( z j 1 ) k 7 / 6 ( Δ z j ) 11 / 6 .
S ( x , y ) = F  -  1 ( M 2 π L S Φ ϕ ( κ ) ) ,
A ( x , y , Z 1 )  =  j = 1 P o exp ( i 2 k z j 1 z j 2 d z ) exp [ i S ( x , y , z j ) ] A ( x , y , z 0 ) ,
A ( x , y , Z 1 + Z 2 )  =  j = 1 P t exp ( i 2 k z j 1 z j 2 d z ) exp [ i S ( x , y , z j ) ] A ( x , y , Z 1 ) t ( x , y ) ,
A ( x , y , Z 0 ) = j = 1 P r exp ( i 2 k z j 1 z j 2 d z ) exp [ i S ( x , y , z j ) ] A ( x , y , z 0 ) ,
G ( x , y ) = 1 N n  =  1 N | A n ( x , y , Z 0 ) | 2 ( ( x , y ) | A n ( x , y , Z 1 + Z 2 ) | 2 ) 1 N n  =  1 N | A n ( x , y , Z 0 ) | 2 1 N n = 1 N ( ( x , y ) | A n ( x , y , Z 1 + Z 2 ) | 2 ) .
CNR = I S I N V S + V N ,
V = I S I N I S  +  I N ,

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