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单像素成像及其在三维重建中的应用

孙鸣捷 张佳敏

孙鸣捷, 张佳敏. 单像素成像及其在三维重建中的应用[J]. 红外与激光工程, 2019, 48(6): 603003-0603003(11). doi: 10.3788/IRLA201948.0603003
引用本文: 孙鸣捷, 张佳敏. 单像素成像及其在三维重建中的应用[J]. 红外与激光工程, 2019, 48(6): 603003-0603003(11). doi: 10.3788/IRLA201948.0603003
Sun Mingjie, Zhang Jiamin. Single-pixel imaging and its application in three-dimensional reconstruction[J]. Infrared and Laser Engineering, 2019, 48(6): 603003-0603003(11). doi: 10.3788/IRLA201948.0603003
Citation: Sun Mingjie, Zhang Jiamin. Single-pixel imaging and its application in three-dimensional reconstruction[J]. Infrared and Laser Engineering, 2019, 48(6): 603003-0603003(11). doi: 10.3788/IRLA201948.0603003

单像素成像及其在三维重建中的应用

doi: 10.3788/IRLA201948.0603003
基金项目: 

国家自然科学基金(61675016)

详细信息
    作者简介:

    孙鸣捷(1982-),男,副教授,博士,主要从事单像素光学成像技术方面的研究。Email:mingjie.sun@buaa.edu.cn

  • 中图分类号: O439

Single-pixel imaging and its application in three-dimensional reconstruction

  • 摘要: 不同于数码相机使用光电探测器阵列来获取图像,单像素成像通过使用一系列掩膜图案对场景进行采样,并将这些掩膜图案中的信息与单像素探测器测量得到的相应光强做关联计算以重建图像。虽然在传统可见光成像领域,单像素成像性能远不如数码相机,但许多研究成果表明,其在复合波长、太赫兹、X射线以及三维成像等一些非常规应用中具有一定优势。介绍了单像素成像技术的发展历程,用数学模型对其成像原理进行了解释,并分析了影响其性能的要点。此外,文中还对三维单像素成像技术的研究工作及其潜在的应用前景进行了总结和展望。
  • [1] Pittman T B, Shih Y H, Strekalov D V, et al. Optical imaging by means of two-photon quantum entanglement[J]. Physical Review A, 1995, 52(5):R3429-R3432.
    [2] Shapiro J H. Computational ghost imaging[J]. Physical Review A, 2008, 78(6):061802.
    [3] Duarte M F, Davenport M A, Takbar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2):83-91.
    [4] Bromberg Y, Katz O, Silberberg Y. Ghost imaging with a single detector[J]. Physical Review A, 2009, 79(5):053840.
    [5] Nipkow P. Optical Disk:German Patent, 30105[P]. 1884-1-6.
    [6] Baird J L. Apparatus for Transmitting Views or Images to a Distance:US, Patent 1699270[P]. 1929-01-15.
    [7] Mertz P, Gray F. Atheory of scanning and its relation to the characteristics of the transmitted signal in telephotography and television[J]. Bell System Technical Journal, 1934, 13(3):464-515.
    [8] Kane T J, Byvik C E, Kozlovsky W J, et al. Coherent laser radar at 1.06m sing Nd:YAG lasers[J]. Optics Letters, 1987, 12(4):239-241.
    [9] Hu B B, Nuss M C. Imaging with terahertz waves[J]. Optics Letters, 1995, 20(16):1716-1718.
    [10] Thibault P, Dierolf M, Menzel A, et al. High-resolution scanning x-ray diffraction microscopy[J]. Science, 2008, 321(5887):379-382.
    [11] Scarcelli G, Berardi V, Shih Y H. Can two-photon correlation of chaotic light be considered as correlation of intensity fluctuations?[J]. Physical Review Letters, 2006, 96(6):063602.
    [12] Shih Y H. Quantum imaging[J]. IEEE Journal on Selected Topics in Quantum Electronics, 2007, 13(4):1016-1030.
    [13] Bennink R S, Bentley S J, Boyd R W. Two-photon coincidence imaging with a classical source[J]. Physical Review Letters, 2002, 89(11):113601.
    [14] Gatti A, Brambilla E, Bache M, et al. Correlated imaging:quantum and classical[J]. Physical Review A, 2004, 70(1):13801-13802.
    [15] Valencia A, Scarcelli G, D'Angelo M, et al. Two-photon imaging with thermal light[J]. Physical Review Letters, 2005, 94(6):063601.
    [16] Zhai Y H, Chen X H, Zhang D, et al. Two-photon interference with true thermal light[J]. Physical Review A, 2005, 72(4):043805.
    [17] Katz O, Bromberg Y, Silberberg Y. Compressive ghost imaging[J]. Applied Physics Letters, 2009, 95(13):131110.
    [18] Erkmen B I, Shapiro J H. Unified theory of ghost imaging with Gaussian-state light[J]. Physical Review A, 2012, 77(4):140-140.
    [19] Shapiro J H, Boyd R W. The physics of ghost imaging[J]. Quantum Information Processing, 2012, 11(4):949-993.
    [20] Altmann Y, McLaughlin S, Padgett M J, et al. Quantum-inspired computational imaging[J]. Science, 2018, 361:6403.
    [21] Cands E J. Compressive sampling[C]//Proceedings of the 2006 International Congress of Mathematicians, 2006:1433-1452.
    [22] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
    [23] Cands E, Romberg J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3):969-985.
    [24] Baraniuk R G. Compressive sensing[lecture notes] [J]. IEEE Signal Processing Magazine, 2007, 24(4):118-120.
    [25] Studer V, Jrome B, Chahid M, et al. Compressive fluorescence microscopy for biological and hyperspectral imaging[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(26):E1679-E1687.
    [26] Welsh S S, Edgar M P, Edgar S S, et al. Fast full-color computational imaging with single-pixel detectors[J]. Optics Express, 2013, 21(20):23068-23074.
    [27] Radwell N, Mitchell K J, Gibson G M, et al. Single-pixel infrared and visible microscope[J]. Optica, 2014, 1(5):285-289.
    [28] Edgar M P, Gibson G M, Bowman R W, et al. Simultaneous real-time visible and infrared video with single-pixel detectors[J]. Scientific Reports, 2015, 5:10669.
    [29] Bian L, Suo J, Situ G, et al. Multispectral imaging using a single bucket detector[J]. Scientific Reports, 2016, 6:24752.
    [30] Watts C M, Shrekenhamer D, Montoya J, et al. Terahertz compressive imaging with metamaterial spatial light modulators[J]. Nature Photonics, 2014, 8(8):605-609.
    [31] Stantchev R I, Sun B, Hornett S M, et al. Noninvasive, near-field terahertz imaging of hidden objects using a single-pixel detector[J]. Science Advances, 2016, 2(6):e1600190.
    [32] Cheng J, Han S. Incoherent coincidence imaging and its applicability in X-ray diffraction[J]. Physical Review Letters, 2004, 92(9):93901-93903.
    [33] Greenberg J, Krishnamurthy K, David B. Compressive single-pixel snapshot X-ray diffraction imaging[J]. Optics Letters, 2014, 39(1):111-114.
    [34] 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.
    [35] Ryczkowski P, Barbier M, Friberg A T, et al. Ghost imaging in the time domain[J]. Nature Photonics, 2016, 10(3):167-170.
    [36] Faccio D. Optical communications:Temporal ghost imaging[J]. Nature Photonics, 2016, 10(3):150-152.
    [37] Devaux F, Moreau P A, Denis S, et al. Computational temporal ghost imaging[J]. Optica, 2016, 3(7):698-701.
    [38] Howland G A, Dixon P B, Howell J C. Photon-counting compressive sensing laser radar for 3D imaging[J]. Applied Optics, 2011, 50(31):5917-5920.
    [39] Zhao C, Gong W, Chen M, et al. Ghost imaging lidar via sparsity constraints[J]. Applied Physics Letters, 2012, 101(14):141123.
    [40] Howland G A, Lum D J, Ware M R, et al. Photon counting compressive depth mapping[J]. Optics Express, 2013, 21(20):23822-23837.
    [41] Zhao C, Gong W, Chen M, et al. Ghost imaging lidar via sparsity constraints in real atmosphere[J]. Optics and Photonics Journal, 2013, 3(2):83-85.
    [42] Sun B, Edgar M P, Bowman R, et al. 3D computational imaging with single-pixel detectors[J]. Science, 2013, 340(6134):844-847.
    [43] Yu H, Li E, Gong W, et al. Structured image reconstruction for three-dimensional ghost imaging lidar[J]. Optics Express, 2015, 23(11):14541-14551.
    [44] Yu W K, Yao X R, Liu X F, et al. Three-dimensional single-pixel compressive reflectivity imaging based on complementary modulation[J]. Applied Optics, 2015, 54(3):363-367.
    [45] Sun M J, Edgar M P, Gibson G M, et al. Single-pixel three-dimensional imaging with time-based depth resolution[J]. Nature Communications, 2016, 7:12010.
    [46] Zhang Z, Zhong J. Three-dimensional single-pixel imaging with far fewer measurements than effective image pixels[J]. Optics Letters, 2016, 41(11):2497-2500.
    [47] Zhang Z B, Liu S J, Peng J Z, et al. Simultaneous spatial, spectral, and 3D compressive imaging via efficient Fourier single-pixel measurements[J]. Optica, 2018, 5(3):315-319.
    [48] Salvador-Balaguer E, Latorre-Carmona P, Chabert C, et al. Low-cost single-pixel 3D imaging by using an LED array[J]. Optics Express, 2018, 26(12):15623-15631.
    [49] Massa J S, Wallace A M, Buller G S, et al. Laser depth measurement based on time-correlated single-photon counting[J]. Optics Letters, 1997, 22(8):543-545.
    [50] McCarthy A, Collins R J, Krichel N J, et al. Long-range time-of-flight scanning sensor based on high-speed time-correlated single-photon counting[J]. Applied Optics, 2009, 48(32):6241-6251.
    [51] McCarthy A, Krichel N J, Gemmell N R, et al. Kilometer-range, high resolution depth imaging via 1560 nm wavelength single-photon detection[J]. Optics Express, 2013, 21(7):8904-8915.
    [52] Lochocki B, Gambn A, Manzanera S, et al. Single pixel camera ophthalmoscope[J]. Optica, 2016, 3(10):1056-1059.
    [53] Sun M J, Edgar M P, Phillips D B, et al. Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning[J]. Optics Express, 2016, 24(10):10476-10485.
    [54] Wang L, Zhao S. Fast reconstructed and high-quality ghost imaging with fast Walsh-Hadamard transform[J]. Photonics Research, 2016, 4(6):240-244.
    [55] Zhang Z, Ma X, Zhong J. Single-pixel imaging by means of Fourier spectrum acquisition[J]. Nature Communications, 2015, 6:6225.
    [56] Czajkowski K M, Pastuszczak A, Kotynski R. Real-time single-pixel video imaging with Fourier domain regularization[J]. Optics Express, 2018, 26(16):20009-20022.
    [57] Amann M, Bayer M. Compressive adaptive computational ghost imaging[J]. Scientific Reports, 2013, 3:1545.
    [58] Yu W K, Li M F, Yao X R, et al. Adaptive compressive ghost imaging based on wavelet trees and sparse representation[J]. Optics Express, 2014, 22(6):7133-7144.
    [59] Rousset F, Ducros N, Farina A, et al. Adaptive basis scan by wavelet prediction for single-pixel imaging[J]. IEEE Transactions on Computational Imaging, 2017, 3(1):36-46.
    [60] Czajkowski K M, Pastuszczak A, Kotyński R. Single-pixel imaging with Morlet wavelet correlated random patterns[J]. Scientific Reports, 2018, 8(1):466.
    [61] Sun M J, Meng L T, Edgar M P, et al. A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging[J]. Scientific Reports, 2017, 7(1):3464.
    [62] Aravind R, Cash G L, Worth J P. On implementing the JPEG still-picture compression algorithm[C]//Advances in Intelligent Robotics Systems Conference, 1989, 1199:799-808.
    [63] Cheng X, Liu Q, Luo K H, et al. Lensless ghost imaging with true thermal light[J]. Optics Letters, 2009, 34(5):695-697.
    [64] Ferri F, Magatti D, Lugiato L, et al. Differential ghost imaging[J]. Physical Review Letters, 2010, 104(25):253603.
    [65] Agafonov I N, Luo K H, Wu L A, et al. High-visibility, high-order lensless ghost imaging with thermal light[J]. Optics Letters, 2010, 35(8):1166-1168.
    [66] Sun B, Welsh S, Edgar M P, et al. Normalized ghost imaging[J]. Optics Express, 2012, 20(15):16892-16901.
    [67] Sun M J, Li M F, Wu L A. Nonlocal imaging of a reflective object using positive and negative correlations[J]. Applied Optics, 2015, 54(25):7494-7499.
    [68] Song S C, Sun M J, Wu L A. Improving the signal-to-noise ratio of thermal ghost imaging based on positive-negative intensity correlation[J]. Optics Communications, 2016, 366:8-12.
    [69] Sun M J, He X, Li M F, et al. Thermal light subwavelength diffraction using positive and negative correlations[J]. Chinese Optics Letters, 2016, 14(4):15-19.
    [70] Candes E J, Tao T. Near-optimal signal recovery from random projections:Universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12):5406-5425.
    [71] Sankaranarayanan A C, Studer C, Baraniuk R G. CS-MUVI:Video compressive sensing for spatial-multiplexing cameras[C]//IEEE International Conference on Computational Photography, 2012:6215212.
    [72] Gong W, Zhao C, Yu H, et al. Three-dimensional ghost imaging lidar via sparsity constraint[J]. Scientific Reports, 2016, 6:26133.
    [73] Xu Z H, Chen W, Penuelas J, et al. 1000 fps computational ghost imaging using LED-based structured illumination[J]. Optics Express, 2018, 26(3):2427-2434.
    [74] Komatsu K, Ozeki Y, Nakano Y, et al. Ghost imaging using integrated optical phased array[C]//Optical Fiber Communication Conference. IEEE, 2017:4.
    [75] Li L J, Chen W, Zhao X Y, et al. Fast Optical phased array calibration technique for random phase modulation LiDAR[J]. IEEE Photonics Journal, 2018, 11(1):1-10.
    [76] Sun M J, Zhao X Y, Li L J. Imaging using hyperuniform sampling with a single-pixel camera[J]. Optics Letters, 2018, 43(16):4049-4052.
    [77] Phillips D B, Sun M J, Taylor J M, et al. Adaptive foveated single-pixel imaging with dynamic super-sampling[J]. Science Advances, 2017, 3(4):1601782.
    [78] Herman M, Tidman J, Hewitt D, et al. A higher-speed compressive sensing camera through multi-diode design[C]//SPIE Defense, Security, Sensing, 2013, 8717:871706.
    [79] Sun M J, Chen W, Liu T F, et al. Image retrieval in spatial and temporal domains with a quadrant detector[J]. IEEE Photonics Journal, 2017, 9(5):1-6.
    [80] Dickson R M, Norris D J, Tzeng Y L, et al. Three-dimensional imaging of single molecules solvated in pores of poly(acrylamide) gels[J]. Science, 1996, 274(5289):966-968.
    [81] Udupa J K, Herman G T. 3D Imaging in Medicine[M]. Boca Raton:CRC Press, 1991.
    [82] Bosch T, Lescure M, Myllyla R, et al. Laser ranging:A critical review of usual techniques for distance measurement[J]. Optical Engineering, 2001, 40(1):10-19.
    [83] Schwarz B. Lidar:Mapping the world in 3D[J]. Nature Photonics, 2010, 4(7):429-430.
    [84] Zhang S. Recent progresses on real-time 3D shape measurement using digital fringe projection techniques[J]. Optics and Lasers in Engineering, 2010, 48(2):149-158.
    [85] Cho M, Javidi B. Three-dimensional photon counting double-random-phase encryption[J]. Optics Letters, 2013, 38(17):3198-3201.
    [86] Velten A, Willwacher T, Gupta O, et al. Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging[J]. Nature Communications, 2012, 3(3):745.
    [87] Keppel E. Approximating complex surfaces by triangulation of contour lines[J]. IBM Journal of Research and Development, 1975, 19(1):2-11.
    [88] Boyde A. Stereoscopic images in confocal (tandem scanning) microscopy[J]. Science, 1985, 230(4731):1270-1272.
    [89] Woodham R J. Photometric method for determining surface orientation from multiple images[J]. Optical Engineering, 1980, 19(1):139-144.
    [90] Horn B K P. Robot Vision[M]. US:MIT Press, 1986.
    [91] Horn B K P, Brooks M J. Shape from Shading[M]. US:MIT Press, 1989.
    [92] Zhang Y, Edgar M P, Sun B, et al. 3D single-pixel video[J]. Journal of Optics, 2016, 18(3):035203.
    [93] Geng J. Structured-light 3D surface imaging:A tutorial[J]. Advances in Optics and Photonics, 2011, 3(2):128-160.
    [94] Jiang C F, Bell T, Zhang S. High dynamic range real-time 3D shape measurement[J]. Optics Express, 2016, 24(7):7337-7346.
    [95] Goda K, Tsia K K, Jalali B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena[J]. Nature, 2009, 458(7242):1145-1149.
    [96] Diebold E D, Buckley B W, Gossett D R, et al. Digitally synthesized beat frequency multiplexing for sub-millisecond fluorescence microscopy[J]. Nature Photonics, 2013, 7(10):806-810.
    [97] Tajahuerce E, Durn V, Clemente P, et al. Image transmission through dynamic scattering media by single-pixel photodetection[J]. Optics Express, 2014, 22(14):16945-16955.
    [98] Guo Q, Chen H W, Weng Z L, et al. Compressive sensing based high-speed time-stretch optical microscopy for two-dimensional image acquisition[J]. Optics Express, 2015, 23(23):29639-29646.
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出版历程
  • 收稿日期:  2019-04-05
  • 修回日期:  2019-05-17
  • 刊出日期:  2019-06-25

单像素成像及其在三维重建中的应用

doi: 10.3788/IRLA201948.0603003
    作者简介:

    孙鸣捷(1982-),男,副教授,博士,主要从事单像素光学成像技术方面的研究。Email:mingjie.sun@buaa.edu.cn

基金项目:

国家自然科学基金(61675016)

  • 中图分类号: O439

摘要: 不同于数码相机使用光电探测器阵列来获取图像,单像素成像通过使用一系列掩膜图案对场景进行采样,并将这些掩膜图案中的信息与单像素探测器测量得到的相应光强做关联计算以重建图像。虽然在传统可见光成像领域,单像素成像性能远不如数码相机,但许多研究成果表明,其在复合波长、太赫兹、X射线以及三维成像等一些非常规应用中具有一定优势。介绍了单像素成像技术的发展历程,用数学模型对其成像原理进行了解释,并分析了影响其性能的要点。此外,文中还对三维单像素成像技术的研究工作及其潜在的应用前景进行了总结和展望。

English Abstract

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