Acta Optica Sinica, Volume. 40, Issue 18, 1810002(2020)

Fringe Pattern Inpainting Based on Convolutional Neural Network Denoising Regularization

Guangze Peng and Wenjing Chen*
Author Affiliations
  • Department of Optic-Electronic, College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan 610065, China
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    Figures & Tables(13)
    Input-output fringes
    Architecture of denoising CNN
    Comparison of highlight areas determined by different methods. (a) Normal exposure time fringe pattern; (b) short exposure time fringe pattern; (c) modulation of Fig. 3(a); (d) modulation of Fig. 3(b); (e) result image of Fig. 3(a) using Otsu threshold method; (f) result image of Fig. 3(b) using Otsu threshold method
    Fusion process of iterative initial value
    Complete flow chart of fringe inpainting
    Results of inpainting. (a) Standard fringe pattern; (b) simulated fringe pattern with highlight region; (c) initial value of the iteration with Gaussian noise; (d) inpainting result of Ref. [19] method; (e) inpainting result of Ref. [20] method; (f) inpainting result of proposed method
    Experimental setup
    Comparison of inpainting results with different initial values. (a) Normal exposure time fringe pattern; (b) inpainting result of Fig. 8(a); (c) initial image-fused by proposed method; (d) inpainting result of Fig. 8(c)
    Fringe pattern inpainting results. (a) Original fringe pattern; (b) initial value for iteration; (c) inpainting result of Ref. [4] method; (d) inpainting result of Ref. [19] method; (e) inpainting result of Ref. [20] method; (f) inpainting result of proposed method
    Phase reconstruction results. (a) Result of Ref. [4] method; (b) result of Ref. [19] method; (c) result of Ref. [20] method; (d) result of iterative initial value; (e) result of proposed method
    Comparsion of gray distribution of fringe pattern under different exposure time. (a) Normal exposure time fringe pattern; (b) short exposure time fringe pattern; (c) inpainting result of proposed method; gray distribution of 170--370 columns in 256 row of (d) normal exposure time fringe pattern and (e) inpainting fringe pattern
    Comparison of reconstruction results. (a) Reconstruction result using normal exposure time fringe pattern; (b) reconstruction result using proposed method inpainting fringe pattern
    • Table 1. Comparison in execution time, PSNR, and RMSE of phase reconstruction with Ref. [19] method, Ref. [20] method, and proposed method

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      Table 1. Comparison in execution time, PSNR, and RMSE of phase reconstruction with Ref. [19] method, Ref. [20] method, and proposed method

      MethodIterationExecution time /sPSNRRMSE
      Initial value compared to ground truth--26.49713.1961
      Ref. [19] method2008.7933.16271.7963
      Ref. [20] method5083.0343.06490.4595
      Proposed method59.81/0.38(on GPU)45.26410.3946
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    Guangze Peng, Wenjing Chen. Fringe Pattern Inpainting Based on Convolutional Neural Network Denoising Regularization[J]. Acta Optica Sinica, 2020, 40(18): 1810002

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    Paper Information

    Category: Image Processing

    Received: Jan. 16, 2020

    Accepted: Jun. 3, 2020

    Published Online: Aug. 27, 2020

    The Author Email: Chen Wenjing (chenwj0409@scu.edu.cn)

    DOI:10.3788/AOS202040.1810002

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