Chinese Journal of Quantum Electronics, Volume. 39, Issue 6, 863(2022)
Progress of ghost imaging algorithms
[1] [1] Sun S, Du L K, Li D, et al. Progress and prospect of ghost imaging in extremely weak light (Invited)[J]. Infrared and Laser Engineering, 2021, 50(12):20210819.
[4] [4] Liu W T, Sun S, Hu H K, et al. Progress and prospect for ghost imaging of moving objects (Invited)[J]. Laser& Optoelectronics Progress, 2021, 58(10): 1011001.
[6] [6] Cheng J, Han S S. Incoherent coincidence imaging and its applicability in X-ray diffraction[J]. Physical Review Letters, 2004, 92(9): 093903.
[7] [7] Gatti A, Brambilla E, Bache M, et al. Ghost imaging with thermal light: Comparing entanglement and classical correlation[J]. Physical Review Letters, 2004, 93(9): 093602.
[8] [8] D’Angelo M, Chekhova M V, Shih Y. Two-photon diffraction and quantum lithography[J]. Physical Review Letters, 2001, 87(1): 013602.
[9] [9] Xiong J, Cao D Z, Huang F, et al. Experimental observation of classical subwavelength interference with a pseudothermal light source[J]. Physical Review Letters, 2005, 94(17): 173601.
[10] [10] Yu H, Lu R H, Han S S, et al. Fourier-transform ghost imaging with hard X rays[J]. Physical Review Letters, 2016, 117(11): 113901.
[11] [11] Pelliccia D, Rack A, Scheel M, et al. Experimental X-ray ghost imaging[J]. Physical Review Letters, 2016, 117(11): 113902.
[12] [12] Klein Y, Schori A, Dolbnya I P, et al. X-ray computational ghost imaging with single-pixel detector[J]. Optics Express, 2019, 27(3): 3284-3293.
[13] [13] Zhang A X, He Y H, Wu L A, et al. Tabletop X-ray ghost imaging with ultra-low radiation[J]. Optica, 2018, 5(4): 374-377.
[14] [14] Yan Y Q, Zhao C Q, Xu W D, et al. Research on the terahertz active ghost imaging technology[J]. Chinese Journal of Lasers, 2018, 45(8): 0814001.
[15] [15] Zha G F. Microwave Coincidence Imaging Technique Research for Moving Target[D]. Changsha: National University of Defense Technology, 2016.
[16] [16] Zhou X L. Theory and Methods of Sparsity-based Microwave Coincidence Imaging[D]. Changsha: National University of Defense Technology, 2017.
[17] [17] Chen Y. The Research of Microwave Correlated Imaging Methods[D]. Xi’an: Xidian University, 2018.
[18] [18] Li S, Cropp F, Kabra K, et al. Electron ghost imaging[J]. Physical Review Letters, 2018, 121(11): 114801.
[19] [19] Khakimov R I, Henson B M, Shin D K, et al. Ghost imaging with atoms[J]. Nature, 2016, 540(7631): 100-103.
[20] [20] He Y H, Huang Y Y, Zeng Z R, et al. Single-pixel imaging with neutrons[J]. Science Bulletin, 2021, 66(2): 133-138.
[21] [21] Kingston A M, Myers G R, Pelliccia D, et al. Neutron ghost imaging[J]. Physical Review A, 2020, 101(5): 053844.
[22] [22] Gong W L, Han S S. Correlated imaging in scattering media[J]. Optics Letters, 2011, 36(3): 394-396.
[23] [23] Xu Y K, Liu W T, Zhang E F, et al. Is ghost imaging intrinsically more powerful against scattering?[J]. Optics Express, 2015, 23(26): 32993-33000.
[24] [24] Tan W, Huang X W, Nan S Q, et al. Effect of the collection range of a bucket detector on ghost imaging through turbulent atmosphere[J]. Journal of the Optical Society of America A, 2019, 36(7): 1261-1266.
[25] [25] Wu J J, Hu L F, Wang J C. Fast tracking and imaging of moving object with single-pixel imaging[J]. Optics Express, 2021, 29(26): 42589-42598.
[26] [26] Sun S, Lin H Z, Xu Y K, et al. Tracking and imaging of moving objects with temporal intensity difference correlation[J]. Optics Express, 2019, 27(20): 27851-27861.[PubMed]
[27] [27] Sun S, Gu J H, Lin H Z, et al. Gradual ghost imaging of moving objects by tracking based on cross correlation[J]. Optics Letters, 2019, 44(22): 5594-5597.
[28] [28] Li D, Yang D, Sun S, et al. Enhancing robustness of ghost imaging against environment noise via cross-correlation in time domain[J]. Optics Express, 2021, 29(20): 31068-31077.
[29] [29] Wang L, Zhao S M. Fast reconstructed and high-quality ghost imaging with fast Walsh-Hadamard transform[J]. Photonics Research, 2016, 4(6): 240-244.
[30] [30] Xi M J, Chen H, Yuan Y, et al. Bi-frequency 3D ghost imaging with Haar wavelet transform[J]. Optics Express, 2019, 27(22): 32349-32359.
[31] [31] Chen Y, Liu S, Yao X R, et al. Discrete cosine single-pixel microscopic compressive imaging via fast binary modulation[J]. Optics Communications, 2020, 454: 124512.
[32] [32] Gu J H, Sun S, Xu Y K, et al. Feedback ghost imaging by gradually distinguishing and concentrating onto the edge area[J]. Chinese Optics Letters, 2021, 19(4): 041102.
[33] [33] Liu B, Wang F, Chen C, et al. Self-evolving ghost imaging[J]. Optica, 2021, 8 (10): 1340.
[34] [34] Sun S, Liu W T, Lin H Z, et al. Multi-scale adaptive computational ghost imaging[J]. Scientific Reports, 2016, 6: 37013.
[35] [35] Ferri F, Magatti D, Lugiato L A, et al. Differential ghost imaging[J]. Physical Review Letters, 2010, 104(25): 253603.
[36] [36] Sun B, Welsh S S, Edgar M P, et al. Normalized ghost imaging[J]. Optics Express, 2012, 20(15): 16892-16901.
[37] [37] Sun S, Liu W T, Gu J H, et al. Ghost imaging normalized by second-order coherence[J]. Optics Letters, 2019, 44(24): 5993-5996.
[38] [38] Katz O, Bromberg Y, Silberberg Y. Compressive ghost imaging[J]. Applied Physics Letters, 2009, 95(13): 131110.
[39] [39] Gong W L, Han S S. Experimental investigation of the quality of lensless super-resolution ghost imaging via sparsity constraints[J]. Physics Letters A, 2012, 376(17): 1519-1522.
[40] [40] Gong W L. High-resolution pseudo-inverse ghost imaging[J]. Photonics Research, 2015, 3(5): 234-237.
[41] [41] Zhang C, Guo S X, Cao J S, et al. Object reconstitution using pseudo-inverse for ghost imaging[J]. Optics Express, 2014, 22(24): 30063-30073.
[42] [42] Lyu M, Wang W, Wang H, et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 2017, 7(1): 17865.
[43] [43] Su F, Liu X, Long H B, et al. Sampling number of image reconstruction arithmetic based on quantum correlated imaging[J]. Chinese Journal of Quantum Electronics, 2015, 32(2): 144-149.
[46] [46] Valencia A, Scarcelli G, D’Angelo M, et al. Two-photon imaging with thermal light[J]. Physical Review Letters, 2005, 94(6): 063601.
[47] [47] Sun M J, Wang H Y, Huang J Y. Improving the performance of computational ghost imaging by using a quadrant detector and digital micro-scanning[J]. Scientific Reports, 2019, 9(1): 4105.
[48] [48] Xie P Y, Shi X H, Huang X W, et al. Binary detection in ghost imaging with preserved grayscale[J]. The European Physical Journal D, 2019, 73(5): 102.
[49] [49] Candès E J. Compressive sampling[C]. Proceedings of the International Congress of Mathematicians, 2006: 1433-1452.
[50] [50] Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91.
[51] [51] Zerom P, Chan K W C, Howell J C, et al. Entangled-photon compressive ghost imaging[J]. Physical Review A, 2011, 84(6): 061804.
[52] [52] Shchepakina E, Korotkova O. Second-order statistics of stochastic electromagnetic beams propagating through non-Kolmogorov turbulence[J]. Optics Express, 2010, 18(10): 10650-10658.
[53] [53] Barbastathis G, Ozcan A, Situ G H. On the use of deep learning for computational imaging[J]. Optica, 2019, 6(8): 921-943.
[54] [54] Sinha A, Lee J, Li S, et al. Lensless computational imaging through deep learning[J]. Optica, 2017, 4(9): 1117-1125.
[55] [55] Xue Y J, Cheng S Y, Li Y Z, et al. Reliable deep-learning-based phase imaging with uncertainty quantification[J]. Optica, 2019, 6(5): 618-629.
[56] [56] Rivenson Y, Liu T R, Wei Z S, et al. PhaseStain: The digital staining of label-free quantitative phase microscopy images using deep learning[J]. Light: Science & Applications, 2019, 8(1): 23.
[57] [57] Li S, Deng M, Lee J, et al. Imaging through glass diffusers using densely connected convolutional networks[J]. Optica, 2018, 5(7): 803-813.
[58] [58] Li Y Z, Xue Y J, Tian L. Deep speckle correlation: A deep learning approach toward scalable imaging through scattering media[J]. Optica, 2018, 5(10): 1181-1190.
[59] [59] Lyu M, Wang H, Li G W, et al. Learning-based lensless imaging through optically thick scattering media[J]. Advanced Photonics, 2019, 1(3): 036002.
[60] [60] He Y C, Wang G, Dong G X, et al. Ghost imaging based on deep learning[J]. Scientific Reports, 2018, 8(1): 6469.
[61] [61] Lyu M, Wang W, Wang H, et al. Deep-learning-based ghost imaging[J]. Scientific Reports, 2017, 7: 17865.
[62] [62] Wang F, Wang H, Wang H C, et al. Learning from simulation: An end-to-end deep-learning approach for computational ghost imaging[J]. Optics Express, 2019, 27(18): 25560-25572.
[63] [63] Hu H K, Sun S, Lin H Z, et al. Denoising ghost imaging under a small sampling rate via deep learning for tracking and imaging moving objects[J]. Optics Express, 2020, 28(25): 37284-37293.
[64] [64] Wang F, Wang C L, Chen M L, et al. Far-field super-resolution ghost imaging with a deep neural network constraint[J]. Light: Science & Applications, 2022, 11: 1.
[65] [65] Zhang H W, Guo S X, Zhang C, et al. Pseudo-inverse iterative denoising method for object reconstruction of ghost imaging[J]. Acta Photonica Sinica, 2017, 46(2): 0210001.
[66] [66] Guo S X, Zhang C, Cao J S, et al. Object reconstruction by compressive sensing based on normalized ghost imaging[J]. Optics and Precision Engineering, 2015, 23(1): 288-294.
Get Citation
Copy Citation Text
LIN Huizu, LIU Weitao, SUN Shuai, DU Longkun, CHANG Chen, LI Yuegang. Progress of ghost imaging algorithms[J]. Chinese Journal of Quantum Electronics, 2022, 39(6): 863
Received: Mar. 3, 2022
Accepted: --
Published Online: Mar. 5, 2023
The Author Email: Huizu LIN (linhuizu2@126.com)