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    Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 18 >Page 1811002 > Article
    • Laser & Optoelectronics Progress
    • Vol. 60, Issue 18, 1811002 (2023)
    Continuous-Wave Terahertz In-Line Digital Holography Based on Physics-Enhanced Deep Neural Network
    Jie Zhao1,2,**, Xiaoyu Jin1, Dayong Wang1,2,*, Lu Rong1,2..., Yunxin Wang1,2 and Shufeng Lin1|Show fewer author(s)
    Author Affiliations
  • 1Faculty of Science, Beijing University of Technology, Beijing 100124, China
  • 2Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing 100124, China
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    DOI: 10.3788/LOP231397 Cite this Article Set citation alerts
    Jie Zhao, Xiaoyu Jin, Dayong Wang, Lu Rong, Yunxin Wang, Shufeng Lin. Continuous-Wave Terahertz In-Line Digital Holography Based on Physics-Enhanced Deep Neural Network[J]. Laser & Optoelectronics Progress, 2023, 60(18): 1811002 Copy Citation Text show less
    The recording schematic of THz in-line digital hologram
    Fig. 1. The recording schematic of THz in-line digital hologram
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    Neural network algorithm based on PhysenNet. (a) The flowchart; (b) the schematic diagram of the U-Net
    Fig. 2. Neural network algorithm based on PhysenNet. (a) The flowchart; (b) the schematic diagram of the U-Net
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    The simulations of in-line digital holography based on the PhysenNet method. (a) (b) The amplitude and phase distributions of the simulated samples; (c) the simulated hologram; (d) the results reconstructed by PhysenNet with different iterations; (e) the loss function curve
    Fig. 3. The simulations of in-line digital holography based on the PhysenNet method. (a) (b) The amplitude and phase distributions of the simulated samples; (c) the simulated hologram; (d) the results reconstructed by PhysenNet with different iterations; (e) the loss function curve
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    Comparison of numerical simulation results of different phase retrieval methods. (a)-(e) and (f)-(j) The reconstructed amplitude and phase distribution by the ASP, ER, IDPR-RI, CCTV, and PhysenNet method, respectively
    Fig. 4. Comparison of numerical simulation results of different phase retrieval methods. (a)-(e) and (f)-(j) The reconstructed amplitude and phase distribution by the ASP, ER, IDPR-RI, CCTV, and PhysenNet method, respectively
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    Schematic of continuous waves THz in-line digital holography
    Fig. 5. Schematic of continuous waves THz in-line digital holography
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    The amplitude distributions of siemens star obtained by different phase retrieval algorithms. (a)-(c) Physical images, holograms, and normalized holograms of samples; (d)-(h) the amplitude distributions by the ASP, ER, IDPR-RI, CCTV, and PhysenNet method, respectively
    Fig. 6. The amplitude distributions of siemens star obtained by different phase retrieval algorithms. (a)-(c) Physical images, holograms, and normalized holograms of samples; (d)-(h) the amplitude distributions by the ASP, ER, IDPR-RI, CCTV, and PhysenNet method, respectively
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    The reconstructed results of the cicada wing by different phase retrieval algorithms. (a) the optical photo of the sample; (f) the normalized hologram; (b)-(e) and (g)-(j) the amplitude and phase distributions by the ER, IDPR-RI, CCTV, and PhysenNet method, respectively
    Fig. 7. The reconstructed results of the cicada wing by different phase retrieval algorithms. (a) the optical photo of the sample; (f) the normalized hologram; (b)-(e) and (g)-(j) the amplitude and phase distributions by the ER, IDPR-RI, CCTV, and PhysenNet method, respectively
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    Comparison of the reconstructed results of a PS foam sphere. (a) The optical photo of the sample; (b) the normalized hologram; (c1)-‍(g1) and (c2)-(g2) the amplitude and phase distributions by the ASP, the ER, the IDPR-RI, the CCTV, and the PhysenNet method, respectively
    Fig. 8. Comparison of the reconstructed results of a PS foam sphere. (a) The optical photo of the sample; (b) the normalized hologram; (c1)-‍(g1) and (c2)-(g2) the amplitude and phase distributions by the ASP, the ER, the IDPR-RI, the CCTV, and the PhysenNet method, respectively
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    AlgorithmPlatformIterationsTime
    ERCPU200~6 s
    IDPR-RICPU50~42 s
    CCTVCPU500~68 s
    PhysenNetCPU10000~430 min
    PhysenNetCPU+GPU10000~8 min
    Table 1. Comparison of time consumption of different phase retrieval algorithms
    Abstract
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    Jie Zhao, Xiaoyu Jin, Dayong Wang, Lu Rong, Yunxin Wang, Shufeng Lin. Continuous-Wave Terahertz In-Line Digital Holography Based on Physics-Enhanced Deep Neural Network[J]. Laser & Optoelectronics Progress, 2023, 60(18): 1811002
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    Paper Information
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