• Laser & Optoelectronics Progress
  • Vol. 58, Issue 10, 1011001 (2021)
Weitao Liu1、2、*†, Shuai Sun1、2、†, Hongkang Hu1、2, and Huizu Lin1、2
Author Affiliations
  • 1Department of Physics, College of Liberal Arts and Science, National University of Defense Technology, Changsha, Hunan 410073, China
  • 2Interdisciplinary Center of Quantum Information, National University of Defense Technology, Changsha, Hunan 410073, China
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    DOI: 10.3788/LOP202158.1011001 Cite this Article Set citation alerts
    Weitao Liu, Shuai Sun, Hongkang Hu, Huizu Lin. Progress and Prospect for Ghost Imaging of Moving Objects[J]. Laser & Optoelectronics Progress, 2021, 58(10): 1011001 Copy Citation Text show less
    Diagram of correlated imaging with pseudo-thermal light
    Fig. 1. Diagram of correlated imaging with pseudo-thermal light
    Experimental setup and results of 1000 frame/s computational ghost imaging[74]. (a) Diagram of imaging system; (b) imaging results of static object; (c) imaging results of rotating object
    Fig. 2. Experimental setup and results of 1000 frame/s computational ghost imaging[74]. (a) Diagram of imaging system; (b) imaging results of static object; (c) imaging results of rotating object
    Two-dimentional pseudo-thermal source using multimode fiber coupled with silicon chip and diagram of imaging setup[80]
    Fig. 3. Two-dimentional pseudo-thermal source using multimode fiber coupled with silicon chip and diagram of imaging setup[80]
    Retinal concave speckleimaging[84]. (a) Field of view with constant resolution; (b) illumination patterns with constant resolution; (c) reconstructed image with constant resolution; (d) retinal concave field of view; (e) retinal concave illumination speckles; (f) reconstructed image from retinal concave speckles
    Fig. 4. Retinal concave speckleimaging[84]. (a) Field of view with constant resolution; (b) illumination patterns with constant resolution; (c) reconstructed image with constant resolution; (d) retinal concave field of view; (e) retinal concave illumination speckles; (f) reconstructed image from retinal concave speckles
    Flowchart of multiresolution scale adaptive correlated imaging[82]
    Fig. 5. Flowchart of multiresolution scale adaptive correlated imaging[82]
    Flowchart of ghost imaging using deep learning[30]
    Fig. 6. Flowchart of ghost imaging using deep learning[30]
    Schematic diagram of CDAE and correlated imaging results of moving object obtained based on machine learning[94]. (a) Schematic diagram of CDAE; (b) results of correlated imaging for moving object
    Fig. 7. Schematic diagram of CDAE and correlated imaging results of moving object obtained based on machine learning[94]. (a) Schematic diagram of CDAE; (b) results of correlated imaging for moving object
    Correlated imaging results for moving object with unknown constant speed[95]. (a) Object; (b) imaging result of stationary object; (c) result of algorithm without compensating speed when object moves at speed of 0.056 pixel/s; (d) imaging result when compensation speed is 0.056 pixel/s; (e) MSE of reconstructed images for different compensation speed
    Fig. 8. Correlated imaging results for moving object with unknown constant speed[95]. (a) Object; (b) imaging result of stationary object; (c) result of algorithm without compensating speed when object moves at speed of 0.056 pixel/s; (d) imaging result when compensation speed is 0.056 pixel/s; (e) MSE of reconstructed images for different compensation speed
    Results of correlated imaging for moving object based on temporal correlation[98]. (a) Objects at different positions; (b) results of conventional correlated imaging; (c) correlated imaging results based on temporal correlation
    Fig. 9. Results of correlated imaging for moving object based on temporal correlation[98]. (a) Objects at different positions; (b) results of conventional correlated imaging; (c) correlated imaging results based on temporal correlation
    Imaging results of temporal differential correlated imaging[101]. (a) Background at time of S0, and objects at different time S1--S4; (b) imaging results of object at S1--S4 time obtained by conventional correlated imaging method; (c) images of moving object at S1--S4 time acquired by temporal differential correlated imaging method
    Fig. 10. Imaging results of temporal differential correlated imaging[101]. (a) Background at time of S0, and objects at different time S1--S4; (b) imaging results of object at S1--S4 time obtained by conventional correlated imaging method; (c) images of moving object at S1--S4 time acquired by temporal differential correlated imaging method
    Diagram of counting principle of ghost cytometry[102]
    Fig. 11. Diagram of counting principle of ghost cytometry[102]
    AlgorithmNumber of samplings requiredQuanlity of reconstructed imageRobustnessTimeliness
    Intensity correlation★ ★ ★★ ★ ★
    Compressive sensing★ ★★ ★
    Machine learning★ ★ ★★ ★ ★★ ★★ ★
    Table 1. Comparison of three algorithms (more stars mean higher performance under this index)
    Weitao Liu, Shuai Sun, Hongkang Hu, Huizu Lin. Progress and Prospect for Ghost Imaging of Moving Objects[J]. Laser & Optoelectronics Progress, 2021, 58(10): 1011001
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