• Acta Optica Sinica
  • Vol. 40, Issue 18, 1810002 (2020)
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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    DOI: 10.3788/AOS202040.1810002 Cite this Article Set citation alerts
    Guangze Peng, Wenjing Chen. Fringe Pattern Inpainting Based on Convolutional Neural Network Denoising Regularization[J]. Acta Optica Sinica, 2020, 40(18): 1810002 Copy Citation Text show less
    Input-output fringes
    Fig. 1. Input-output fringes
    Architecture of denoising CNN
    Fig. 2. 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
    Fig. 3. 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
    Fig. 4. Fusion process of iterative initial value
    Complete flow chart of fringe inpainting
    Fig. 5. 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
    Fig. 6. 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
    Fig. 7. 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)
    Fig. 8. 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
    Fig. 9. 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
    Fig. 10. 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
    Fig. 11. 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
    Fig. 12. Comparison of reconstruction results. (a) Reconstruction result using normal exposure time fringe pattern; (b) reconstruction result using proposed method inpainting fringe pattern
    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
    Table 1. Comparison in execution time, PSNR, and RMSE of phase reconstruction with Ref. [19] method, Ref. [20] method, and proposed method
    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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