• Laser & Optoelectronics Progress
  • Vol. 57, Issue 10, 101105 (2020)
Wei Feng1、2、*, Xiaodong Zhao1, Shaojing Tang1, and Daxing Zhao1
Author Affiliations
  • 1School of Mechanical Engineering, Hubei University of Technology, Wuhan, Hubei 430068, China
  • 2Hubei Key Laboratory of Modern Manufacturing Quality Engineering, Wuhan, Hubei 430068, China
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    DOI: 10.3788/LOP57.101105 Cite this Article Set citation alerts
    Wei Feng, Xiaodong Zhao, Shaojing Tang, Daxing Zhao. Compressive Computational Ghost Imaging Method Based on Region Segmentation[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101105 Copy Citation Text show less
    Schematic of RSCCGI
    Fig. 1. Schematic of RSCCGI
    Flow chart of proposed method
    Fig. 2. Flow chart of proposed method
    Simulation results. (a) Measured object; (b) binary random speckle pattern preset in DMD
    Fig. 3. Simulation results. (a) Measured object; (b) binary random speckle pattern preset in DMD
    ROI recognition results with different sampling rates. (a) β=0.05; (b) β=0.10
    Fig. 4. ROI recognition results with different sampling rates. (a) β=0.05; (b) β=0.10
    Results of image segmentation
    Fig. 5. Results of image segmentation
    Comparison chart of numerical simulation results of different methods at different sampling rates. (a) β=0.05; (b) β=0.10; (c) β=0.20; (d) β=0.30
    Fig. 6. Comparison chart of numerical simulation results of different methods at different sampling rates. (a) β=0.05; (b) β=0.10; (c) β=0.20; (d) β=0.30
    Curves of different indicators and sampling times. (a) PSNR; (b) SSIM
    Fig. 7. Curves of different indicators and sampling times. (a) PSNR; (b) SSIM
    Wei Feng, Xiaodong Zhao, Shaojing Tang, Daxing Zhao. Compressive Computational Ghost Imaging Method Based on Region Segmentation[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101105
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