Ghost imaging through inhomogeneous turbulent atmosphere along an uplink path and a downlink path

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.

[1]  T. Aulin.  A modified model for the fading signal at a mobile radio channel , 1979, IEEE Transactions on Vehicular Technology.

[2]  S. Flatté,et al.  Intensity images and statistics from numerical simulation of wave propagation in 3-D random media. , 1988, Applied optics.

[3]  Shih,et al.  Observation of two-photon "ghost" interference and diffraction. , 1995, Physical review letters.

[4]  Shih,et al.  Optical imaging by means of two-photon quantum entanglement. , 1995, Physical review. A, Atomic, molecular, and optical physics.

[5]  A. Gatti,et al.  Correlated imaging, quantum and classical , 2003, quant-ph/0307187.

[6]  A. Gatti,et al.  Ghost imaging with thermal light: comparing entanglement and classical correlation. , 2003, Physical review letters.

[7]  Shensheng Han,et al.  Ghost imaging with thermal light by third-order correlation , 2007 .

[8]  R. Boyd,et al.  High-order thermal ghost imaging , 2009, 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference.

[9]  Jeffrey H. Shapiro,et al.  Computational ghost imaging , 2008, 2009 Conference on Lasers and Electro-Optics and 2009 Conference on Quantum electronics and Laser Science Conference.

[10]  Jing Cheng.  Ghost imaging through turbulent atmosphere. , 2009, Optics express.

[11]  A. Gatti,et al.  Differential ghost imaging. , 2010, Physical review letters.

[12]  Wenlin Gong,et al.  Correlated imaging through atmospheric turbulence , 2010, 1005.5011.

[13]  D. Simon,et al.  Quantum ghost imaging through turbulence , 2011, 1102.3358.

[14]  D. Simon,et al.  Theoretical analysis of quantum ghost imaging through turbulence , 2011 .

[15]  J. Shapiro,et al.  Reflective ghost imaging through turbulence , 2011, 1110.0845.

[16]  Y. Shih,et al.  Positive-negative turbulence-free ghost imaging , 2012 .

[17]  S. M. Zhao,et al.  Aberration corrections for free-space optical communications in atmosphere turbulence using orbital angular momentum states. , 2012, Optics Express.

[18]  Wenlin Gong,et al.  Ghost imaging lidar via sparsity constraints , 2012, 1203.3835.

[19]  Yi-xin Zhang,et al.  Computational lensless ghost imaging in a slant path non-Kolmogorov turbulent atmosphere , 2012 .

[20]  B. Erkmen.  Computational ghost imaging for remote sensing. , 2012, Journal of the Optical Society of America. A, Optics, image science, and vision.

[21]  Unified theory of thermal ghost imaging and ghost diffraction through turbulent atmosphere , 2013 .

[22]  Yi-xin Zhang,et al.  Lens ghost imaging in a non-Kolmogorov slant turbulence atmosphere , 2013 .

[23]  Ling-An Wu,et al.  Time-correspondence differential ghost imaging , 2013, 1301.4390.

[24]  B. Zheng,et al.  Improving the Atmosphere Turbulence Tolerance in Holographic Ghost Imaging System by Channel Coding , 2013, Journal of Lightwave Technology.

[25]  Wenlin Gong,et al.  Ghost Imaging Lidar via Sparsity Constraints in Real Atmosphere , 2013 .

[26]  G. Gbur.  Partially coherent beam propagation in atmospheric turbulence [invited]. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[27]  Chi Zhang,et al.  Object reconstitution using pseudo-inverse for ghost imaging. , 2014, Optics express.

[28]  Yangjian Cai,et al.  Experimental study of turbulence-induced beam wander and deformation of a partially coherent beam. , 2014, Optics letters.

[29]  Wenlin Gong.  High-resolution pseudo-inverse ghost imaging , 2015 .

[30]  Yuan Zhao,et al.  Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method* , 2015 .

[31]  Hong-Yi Dai,et al.  Is ghost imaging intrinsically more powerful against scattering? , 2015, Optics express.

[32]  Mansour Eslami,et al.  High quality computational ghost imaging using multi-fluorescent screen. , 2015, Journal of the Optical Society of America. A, Optics, image science, and vision.

[33]  X. Ji,et al.  Energy focusability of annular beams propagating through atmospheric turbulence along a slanted path , 2015 .

[34]  D. Paganin,et al.  Experimental X-Ray Ghost Imaging. , 2016, Physical review letters.

[35]  Yanfeng Bai,et al.  Negative influence of detector noise on ghost imaging based on the photon counting technique at low light levels. , 2017, Applied optics.

[36]  Hongchao Liu,et al.  Computational ghost imaging of hot objects in long-wave infrared range , 2017 .

[37]  Guohai Situ,et al.  Deep-learning-based ghost imaging , 2017, Scientific Reports.

[38]  Li Fu,et al.  A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features , 2018, Future Generation Computer Systems.

[39]  Yangjian Cai,et al.  Effects of Atmospheric Turbulence on Lensless Ghost Imaging with Partially Coherent Light , 2018, Applied Sciences.

[40]  Zhuo Xu,et al.  Ghost Imaging Based on Deep Learning , 2018, Scientific Reports.

[41]  L. Tang,et al.  Effects of incident angles on reflective ghost imaging through atmospheric turbulence , 2018 .

[42]  Chunling Luo,et al.  Long-distance ghost imaging with an almost non-diffracting Lorentz source in atmospheric turbulence , 2018, Laser Physics Letters.

[43]  Sophia Chen.  X-ray 'ghost images' could cut radiation doses. , 2018, Science.

[44]  Yanfeng Bai,et al.  Image quality enhancement in low-light-level ghost imaging using modified compressive sensing method , 2018 .

[45]  Heyan Huang,et al.  High-quality compressive ghost imaging , 2018 .

[46]  Shulin Wang,et al.  Feature selection in machine learning: A new perspective , 2018, Neurocomputing.

[47]  Kenli Li,et al.  Multiple convolutional neural networks for multivariate time series prediction , 2019, Neurocomputing.

[48]  Philip S. Yu,et al.  A Bi-layered Parallel Training Architecture for Large-Scale Convolutional Neural Networks , 2018, IEEE Transactions on Parallel and Distributed Systems.

[49]  Li Fu,et al.  A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information , 2019, IEEE Access.

[50]  Yanfeng Bai,et al.  Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere. , 2019, Journal of the Optical Society of America. A, Optics, image science, and vision.

[51]  Yang Zhou,et al.  Enhancing image quality of ghost imaging by fuzzy c-means clustering method , 2019 .

[52]  Fei Wang,et al.  Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging. , 2019, Optics express.