Acta Optica Sinica, Volume. 41, Issue 23, 2312001(2021)

Absolute Phase Recovery of Single Frame Composite Image Based on Convolutional Neural Network

Wenjian Li1,2, Shaoyan Gai1,2、*, Jian Yu1,2, and Feipeng Da1,2,3、**
Author Affiliations
  • 1School of Automation, Southeast University, Nanjing, Jiangsu 210096, China
  • 2Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing, Jiangsu 210096, China
  • 3Shenzhen Research Institute, Southeast University, Shenzhen, Guangdong 518063, China
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    Figures & Tables(14)
    Principle diagram of absolute phase is obtained by using phase shift method combined with gray code method
    Design of composite fringe pattern. (a) Generation principle of composite fringe pattern; (b) composite fringe pattern
    Principle of proposed method
    Structure of CNN 1
    Structure of residual block network
    Structure of CNN 2
    Partial training data of CNN
    Loss function curves of CNN 1 and CNN 2 under different conditions. (a) Loss curves of molecular term M in CNN 1; (b) loss curves of denominator D in CNN 1; (c) loss curves of order k in CNN 2
    Output results of CNN. (a) Measured object; (b) wrapping phase obtained by CNN 1; (c) fringe order obtained by CNN 2
    Wrapped phase error diagram of different methods. (a) Wrapped phase error of proposed method; (b) wrapped phase error of WFT method
    Absolute phase error under different methods. (a) Absolute phase obtained by phase shift method combined with gray code method; (b) absolute phase obtained by proposed method; (c) absolute phase error obtained by proposed method compared with traditional method
    Statistical diagram of absolute phase error under different conditions. (a) Fig. 11(c) image 1; (b) Fig. 11(c) image 2; (c) Fig. 11(c) image 3; (d) Fig. 11(c) image 4
    Phase diagram of open hand process
    Phase diagram of paper tearing process
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    Wenjian Li, Shaoyan Gai, Jian Yu, Feipeng Da. Absolute Phase Recovery of Single Frame Composite Image Based on Convolutional Neural Network[J]. Acta Optica Sinica, 2021, 41(23): 2312001

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Apr. 14, 2021

    Accepted: Jun. 10, 2021

    Published Online: Nov. 29, 2021

    The Author Email: Gai Shaoyan (qxxymm@163.com), Da Feipeng (dafp@seu.edu.cn)

    DOI:10.3788/AOS202141.2312001

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