Please wait a minute...
Chin. Phys. B, 2015, Vol. 24(12): 124202    DOI: 10.1088/1674-1056/24/12/124202
ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS Prev   Next  

Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method

Yang Xu (杨旭), Zhang Yong (张勇), Xu Lu (徐璐), Yang Cheng-Hua (杨成华), Wang Qiang (王强), Liu Yue-Hao (刘越豪), Zhao Yuan (赵远)
Department of Physics, Harbin Institute of Technology, Harbin 150001, China
Abstract  The range accuracy of three-dimensional (3D) ghost imaging is derived. Based on the derived range accuracy equation, the relationship between the slicing number and the range accuracy is analyzed and an optimum slicing number (OSN) is determined. According to the OSN, an improved 3D ghost imaging algorithm is proposed to increase the range accuracy. Experimental results indicate that the slicing number can affect the range accuracy significantly and the highest range accuracy can be achieved if the 3D ghost imaging system works with OSN.
Keywords:  ghost imaging      range accuracy      optimum slicing number  
Received:  28 April 2015      Revised:  07 July 2015      Accepted manuscript online: 
PACS:  42.30.Va (Image forming and processing)  
  42.50.Dv (Quantum state engineering and measurements)  
Fund: Project supported by the Young Scientist Fund of the National Natural Science Foundation of China (Grant No. 61108072).
Corresponding Authors:  Zhao Yuan     E-mail:  zhaoyuan@hit.edu.cn

Cite this article: 

Yang Xu (杨旭), Zhang Yong (张勇), Xu Lu (徐璐), Yang Cheng-Hua (杨成华), Wang Qiang (王强), Liu Yue-Hao (刘越豪), Zhao Yuan (赵远) Increasing the range accuracy of three-dimensional ghost imaging ladar using optimum slicing number method 2015 Chin. Phys. B 24 124202

[1] Gatti A, Brambilla E and Lugiato L A 2004 Phys. Rev. Lett. 93 093602
[2] Gatti A, Bache M and Magatti D 2006 J. Mod. Opt. 53 739
[3] Valencia A, Scarcelli G and D' Angelo M 2005 Phys. Rev. Lett. 94 063601
[4] Gatti A, Magatti D and Bache M 2005 Phys. Rev. Lett. 94 183602
[5] Basano L and Ottonello P 2006 Appl. Phys. Lett. 89 091109
[6] Erkmen B I and Shapiro J H 2008 Phys. Rev. A 77 043809
[7] Erkmen B I and Shapiro J H 2009 Phys. Rev. A 79 023833
[8] Bromberg Y, Katz O and Silberberg Y 2009 Phys. Rev. A 79 053840
[9] Hardy N D and Shapiro J H 2011 Phys. Rev. A 84 063824
[10] Cheng J 2009 Opt. Express 17 7916
[11] Shi D, Fan C and Zhang P 2013 Opt. Express 21 2050
[12] Chan K W, O'Sullivan M N and Boyd R W 2009 Phys. Rev. A 79 033808
[13] Duan D and Xia Y 2014 J. Opt. Soc. Am. 31 183
[14] Li L Z, Yao X R and Liu X F 2014 Acta Phys. Sin. 63 224201 (in Chinese)
[15] Gong W, Zhao C and Jiao J 2013 arXiv:1301.5767 [quant-ph]
[16] Hong Y, Li E R and Gong W L 2015 Opt. Express 23 14541
[17] Bache M, Brambilla E and Gatti A 2004 Opt. Express 12 6067
[18] Cheng J, Han S and Yan Y 2006 Chin. Phys. 15 2002
[19] Erkmen B I and Shapiro J H 2009 Phys. Rev. A 79 023833
[20] Hardy N D and Shapiro J H 2010 SPIE Optical Engineering+Applications, August 30, 2010, San Diego, California, USA, p. 78150L
[1] A probability theory for filtered ghost imaging
Zhong-Yuan Liu(刘忠源), Shao-Ying Meng(孟少英), and Xi-Hao Chen(陈希浩). Chin. Phys. B, 2023, 32(4): 044204.
[2] Ghost imaging based on the control of light source bandwidth
Zhao-Qi Liu(刘兆骐), Yan-Feng Bai(白艳锋), Xuan-Peng-Fan Zou(邹璇彭凡), Li-Yu Zhou(周立宇), Qin Fu(付芹), and Xi-Quan Fu(傅喜泉). Chin. Phys. B, 2023, 32(3): 034210.
[3] Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging
Hui Guo(郭辉), Le Wang(王乐), and Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2022, 31(8): 084201.
[4] Orthogonal-triangular decomposition ghost imaging
Jin-Fen Liu(刘进芬), Le Wang(王乐), and Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2022, 31(8): 084202.
[5] Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm
Haipeng Zhang(张海鹏), Ke Li(李可), Changzhe Zhao(赵昌哲), Jie Tang(汤杰), and Tiqiao Xiao(肖体乔). Chin. Phys. B, 2022, 31(6): 064202.
[6] Iterative filtered ghost imaging
Shao-Ying Meng(孟少英), Mei-Yi Chen(陈美伊), Jie Ji(季杰), Wei-Wei Shi(史伟伟), Qiang Fu(付强), Qian-Qian Bao(鲍倩倩), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2022, 31(2): 028702.
[7] Full color ghost imaging by using both time and code division multiplexing technologies
Le Wang(王乐), Hui Guo(郭辉), and Shengmei Zhao(赵生妹). Chin. Phys. B, 2022, 31(11): 114202.
[8] High speed ghost imaging based on a heuristic algorithm and deep learning
Yi-Yi Huang(黄祎祎), Chen Ou-Yang(欧阳琛), Ke Fang(方可), Yu-Feng Dong(董玉峰), Jie Zhang(张杰), Li-Ming Chen(陈黎明), and Ling-An Wu(吴令安). Chin. Phys. B, 2021, 30(6): 064202.
[9] Handwritten digit recognition based on ghost imaging with deep learning
Xing He(何行), Sheng-Mei Zhao(赵生妹), and Le Wang(王乐). Chin. Phys. B, 2021, 30(5): 054201.
[10] Ghost imaging-based optical cryptosystem for multiple images using integral property of the Fourier transform
Yi Kang(康祎), Leihong Zhang(张雷洪), Hualong Ye(叶华龙), Dawei Zhang(张大伟), and Songlin Zhuang(庄松林). Chin. Phys. B, 2021, 30(12): 124207.
[11] Computational ghost imaging with deep compressed sensing
Hao Zhang(张浩), Yunjie Xia(夏云杰), and Deyang Duan(段德洋). Chin. Phys. B, 2021, 30(12): 124209.
[12] Compressed ghost imaging based on differential speckle patterns
Le Wang(王乐), Shengmei Zhao(赵生妹). Chin. Phys. B, 2020, 29(2): 024204.
[13] Super-resolution filtered ghost imaging with compressed sensing
Shao-Ying Meng(孟少英), Wei-Wei Shi(史伟伟), Jie Ji(季杰), Jun-Jie Tao(陶俊杰), Qian Fu(付强), Xi-Hao Chen(陈希浩), and Ling-An Wu(吴令安). Chin. Phys. B, 2020, 29(12): 128704.
[14] Experimental demonstration of influence of underwater turbulence on ghost imaging
Man-Qian Yin(殷曼倩), Le Wang(王乐), Sheng-Mei Zhao(赵生妹). Chin. Phys. B, 2019, 28(9): 094201.
[15] Mask-based denoising scheme for ghost imaging
Yang Zhou(周阳), Shu-Xu Guo(郭树旭), Fei Zhong(钟菲), Tian Zhang(张天). Chin. Phys. B, 2019, 28(8): 084204.
No Suggested Reading articles found!