Abstract

Fringe projection profilometry (i.e., FPP) has been one of the most popular 3-D measurement techniques. The phase error due to system random noise becomes non-ignorable when fringes captured by a camera have a low fringe modulation, which are inevitable for objects’ surface with un-uniform reflectivity. The phase calculated from these low-modulation fringes may have a non-ignorable phase error and generate 3-D measurement error. Traditional methods reduce the phase error with losing details of 3-D shapes or sacrificing the measurement speed. In this paper, a deep learning-based fringe modulation-enhancing method (i.e., FMEM) is proposed, that transforms two low-modulation fringes with different phase shifts into a set of three phase-shifted high-modulation fringes. FMEM enables to calculate the desired phase from the transformed set of high-modulation fringes, and result in accurate 3-D FPP without sacrificing the speed. Experimental analysis verifies its effectiveness and accurateness.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  1. S. S. Gorthi and P. Rastogi, “Fringe projection techniques: Whither we are?” Opt. Lasers Eng. 48(2), 133–140 (2010).
    [Crossref]
  2. X. Liu, X. Peng, H. Chen, D. He, and B. Z. Gao, “Strategy for automatic and complete three-dimensional optical digitization,” Opt. Lett. 37(15), 3126–3128 (2012).
    [Crossref]
  3. Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
    [Crossref]
  4. F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
    [Crossref]
  5. D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase-shifting profilometry combined with gray-code patterns projection: unwrapping error removal by an adaptive median filter,” Opt. Express 25(5), 4700–4713 (2017).
    [Crossref]
  6. X. Su and Q. Zhang, “Dynamic 3-d shape measurement method: A review,” Opt. Lasers Eng. 48(2), 191–204 (2010).
    [Crossref]
  7. Y. An, J.-S. Hyun, and S. Zhang, “Pixel-wise absolute phase unwrapping using geometric constraints of structured light system,” Opt. Express 24(16), 18445–18459 (2016).
    [Crossref]
  8. J. Zhong and J. Weng, “Spatial carrier-fringe pattern analysis by means of wavelet transform: wavelet transform profilometry,” Appl. Opt. 43(26), 4993–4998 (2004).
    [Crossref]
  9. X. Su and W. Chen, “Fourier transform profilometry:: a review,” Opt. Lasers Eng. 35(5), 263–284 (2001).
    [Crossref]
  10. Q. Zhang and X. Su, “High-speed optical measurement for the drumhead vibration,” Opt. Express 13(8), 3110–3116 (2005).
    [Crossref]
  11. S. Zhang and P. S. Huang, “Phase error compensation for a 3-D shape measurement system based on the phase-shifting method,” Opt. Eng. 46, 60000E (2007).
    [Crossref]
  12. H. Guo, H. He, and M. Chen, “Gamma correction for digital fringe projection profilometry,” Appl. Opt. 43(14), 2906–2914 (2004).
    [Crossref]
  13. F. Lü, S. Xing, and H. Guo, “Self-correction of projector nonlinearity in phase-shifting fringe projection profilometry,” Appl. Opt. 56(25), 7204–7216 (2017).
    [Crossref]
  14. C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
    [Crossref]
  15. Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
    [Crossref]
  16. C. Rathjen, “Statistical properties of phase-shift algorithms,” J. Opt. Soc. Am. A 12(9), 1997–2008 (1995).
    [Crossref]
  17. H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: A novel 3-d scanning technique for high-reflective surfaces,” Opt. Lasers Eng. 50(10), 1484–1493 (2012).
    [Crossref]
  18. S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
    [Crossref]
  19. T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
    [Crossref]
  20. S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
    [Crossref]
  21. W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
    [Crossref]
  22. S. Zhang, “High-speed 3d shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
    [Crossref]
  23. Q. Kemao, “Windowed fourier transform for fringe pattern analysis,” Appl. Opt. 43(13), 2695–2702 (2004).
    [Crossref]
  24. Q. Kemao, H. Wang, and W. Gao, “Windowed fourier transform for fringe pattern analysis: theoretical analyses,” Appl. Opt. 47(29), 5408–5419 (2008).
    [Crossref]
  25. B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
    [Crossref]
  26. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.
  27. C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
    [Crossref]
  28. F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp 815–823.
  29. D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), pp. 3642–3649.
  30. K. G. Lore, A. Akintayo, and S. Sarkar, “Llnet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit. 61, 650–662 (2017).
    [Crossref]
  31. H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
    [Crossref]
  32. S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
    [Crossref]
  33. S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
    [Crossref]
  34. H. Nguyen, N. Dunne, H. Li, Y. Wang, and Z. Wang, “Real-time 3d shape measurement using 3lcd projection and deep machine learning,” Appl. Opt. 58(26), 7100–7109 (2019).
    [Crossref]
  35. K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
    [Crossref]
  36. K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
    [Crossref]
  37. S. Zhang, D. V. D. Weide, and J. Oliver, “Superfast phase-shifting method for 3-d shape measurement,” Opt. Express 18(9), 9684–9689 (2010).
    [Crossref]
  38. S. Zhang, “Recent progresses on real-time 3d shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
    [Crossref]
  39. J. Li, L. G. Hassebrook, and C. Guan, “Optimized two-frequency phase-measuring-profilometry light-sensor temporal-noise sensitivity,” J. Opt. Soc. Am. A 20(1), 106–115 (2003).
    [Crossref]
  40. S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Opt. Eng. 48, 70660A (2009).
    [Crossref]
  41. D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
    [Crossref]
  42. C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
    [Crossref]
  43. C. Chen, Y. Wan, and Y. Cao, “Instability of projection light source and real-time phase error correction method for phase-shifting profilometry,” Opt. Express 26(4), 4258–4270 (2018).
    [Crossref]
  44. Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.
  45. O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.
  46. D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase error analysis and compensation for phase shifting profilometry with projector defocusing,” Appl. Opt. 55(21), 5721–5728 (2016).
    [Crossref]
  47. W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
    [Crossref]
  48. S. Zhang, “Absolute phase retrieval methods for digital fringe projection profilometry: A review,” Opt. Lasers Eng. 107, 28–37 (2018).
    [Crossref]
  49. Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
    [Crossref]
  50. K. Liu, Y. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, “Dual-frequency pattern scheme for high-speed 3-d shape measurement,” Opt. Express 18(5), 5229–5244 (2010).
    [Crossref]
  51. E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
    [Crossref]
  52. O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds. (Springer International Publishing, Cham, 2015), pp. 234–241.
  53. J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).
  54. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of The 32nd International Conference on Machine Learning, (2015), 448–456.
  55. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
    [Crossref]
  56. V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 807–814.
  57. D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).
  58. S. Lei and S. Zhang, “Flexible 3-d shape measurement using projector defocusing,” Opt. Lett. 34(20), 3080–3082 (2009).
    [Crossref]
  59. S. Lei and S. Zhang, “Digital sinusoidal fringe pattern generation: Defocusing binary patterns vs focusing sinusoidal patterns,” Opt. Lasers Eng. 48(5), 561–569 (2010).
    [Crossref]
  60. V. Srinivasan, H. C. Liu, and M. Halioua, “Automated phase-measuring profilometry of 3-d diffuse objects,” Appl. Opt. 23(18), 3105–3108 (1984).
    [Crossref]
  61. S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
    [Crossref]
  62. X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
    [Crossref]

2020 (5)

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

2019 (9)

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
[Crossref]

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

H. Nguyen, N. Dunne, H. Li, Y. Wang, and Z. Wang, “Real-time 3d shape measurement using 3lcd projection and deep machine learning,” Appl. Opt. 58(26), 7100–7109 (2019).
[Crossref]

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

2018 (6)

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

S. Zhang, “High-speed 3d shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

S. Zhang, “Absolute phase retrieval methods for digital fringe projection profilometry: A review,” Opt. Lasers Eng. 107, 28–37 (2018).
[Crossref]

C. Chen, Y. Wan, and Y. Cao, “Instability of projection light source and real-time phase error correction method for phase-shifting profilometry,” Opt. Express 26(4), 4258–4270 (2018).
[Crossref]

2017 (3)

2016 (4)

Y. An, J.-S. Hyun, and S. Zhang, “Pixel-wise absolute phase unwrapping using geometric constraints of structured light system,” Opt. Express 24(16), 18445–18459 (2016).
[Crossref]

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase error analysis and compensation for phase shifting profilometry with projector defocusing,” Appl. Opt. 55(21), 5721–5728 (2016).
[Crossref]

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

2015 (1)

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

2014 (2)

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

2012 (3)

2010 (6)

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: Whither we are?” Opt. Lasers Eng. 48(2), 133–140 (2010).
[Crossref]

X. Su and Q. Zhang, “Dynamic 3-d shape measurement method: A review,” Opt. Lasers Eng. 48(2), 191–204 (2010).
[Crossref]

S. Zhang, D. V. D. Weide, and J. Oliver, “Superfast phase-shifting method for 3-d shape measurement,” Opt. Express 18(9), 9684–9689 (2010).
[Crossref]

S. Zhang, “Recent progresses on real-time 3d shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
[Crossref]

K. Liu, Y. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, “Dual-frequency pattern scheme for high-speed 3-d shape measurement,” Opt. Express 18(5), 5229–5244 (2010).
[Crossref]

S. Lei and S. Zhang, “Digital sinusoidal fringe pattern generation: Defocusing binary patterns vs focusing sinusoidal patterns,” Opt. Lasers Eng. 48(5), 561–569 (2010).
[Crossref]

2009 (2)

S. Lei and S. Zhang, “Flexible 3-d shape measurement using projector defocusing,” Opt. Lett. 34(20), 3080–3082 (2009).
[Crossref]

S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Opt. Eng. 48, 70660A (2009).
[Crossref]

2008 (1)

2007 (1)

S. Zhang and P. S. Huang, “Phase error compensation for a 3-D shape measurement system based on the phase-shifting method,” Opt. Eng. 46, 60000E (2007).
[Crossref]

2005 (1)

2004 (3)

2003 (1)

2001 (1)

X. Su and W. Chen, “Fourier transform profilometry:: a review,” Opt. Lasers Eng. 35(5), 263–284 (2001).
[Crossref]

2000 (1)

F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

1995 (1)

1984 (1)

Akintayo, A.

K. G. Lore, A. Akintayo, and S. Sarkar, “Llnet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit. 61, 650–662 (2017).
[Crossref]

Álvarez, J. M.

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

An, Y.

Arroyo, R.

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

Asundi, A.

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Ba, J.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Bai, L.

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

Bergasa, L. M.

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

Brown, G. M.

F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds. (Springer International Publishing, Cham, 2015), pp. 234–241.

Budzan, V.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

Cao, Y.

Chen, C.

Chen, F.

F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Chen, H.

Chen, M.

Chen, Q.

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
[Crossref]

Chen, W.

X. Su and W. Chen, “Fourier transform profilometry:: a review,” Opt. Lasers Eng. 35(5), 263–284 (2001).
[Crossref]

Chen, X.

Christopoulos, G.

X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
[Crossref]

Ciregan, D.

D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), pp. 3642–3649.

Da, F.

Dong, C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

Dunne, N.

Fan, Y.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Feng, F.

Feng, S.

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
[Crossref]

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds. (Springer International Publishing, Cham, 2015), pp. 234–241.

Fu, S.

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

Gao, B. Z.

Gao, W.

Gorthi, S. S.

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: Whither we are?” Opt. Lasers Eng. 48(2), 133–140 (2010).
[Crossref]

Gu, G.

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
[Crossref]

Guan, C.

Guo, H.

Guo, W.

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

Halioua, M.

Han, J.

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

Hao, Q.

Hassebrook, L. G.

He, D.

He, H.

He, K.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.

He, X.

X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
[Crossref]

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

Hinton, G. E.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 807–814.

Honghai, Sun

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Hu, Y.

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

Huang, C.

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Huang, L.

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

Huang, P. S.

S. Zhang and P. S. Huang, “Phase error compensation for a 3-D shape measurement system based on the phase-shifting method,” Opt. Eng. 46, 60000E (2007).
[Crossref]

Huang, T. S.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Hyun, J.-S.

Ioffe, S.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of The 32nd International Conference on Machine Learning, (2015), 448–456.

Jiang, H.

H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: A novel 3-d scanning technique for high-reflective surfaces,” Opt. Lasers Eng. 50(10), 1484–1493 (2012).
[Crossref]

Kalenichenko, D.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp 815–823.

Kemao, Q.

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
[Crossref]

D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase-shifting profilometry combined with gray-code patterns projection: unwrapping error removal by an adaptive median filter,” Opt. Express 25(5), 4700–4713 (2017).
[Crossref]

D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase error analysis and compensation for phase shifting profilometry with projector defocusing,” Appl. Opt. 55(21), 5721–5728 (2016).
[Crossref]

Q. Kemao, H. Wang, and W. Gao, “Windowed fourier transform for fringe pattern analysis: theoretical analyses,” Appl. Opt. 47(29), 5408–5419 (2008).
[Crossref]

Q. Kemao, “Windowed fourier transform for fringe pattern analysis,” Appl. Opt. 43(13), 2695–2702 (2004).
[Crossref]

Kingma, D. P.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

Kupyn, O.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

Lau, D. L.

Lei, S.

S. Lei and S. Zhang, “Digital sinusoidal fringe pattern generation: Defocusing binary patterns vs focusing sinusoidal patterns,” Opt. Lasers Eng. 48(5), 561–569 (2010).
[Crossref]

S. Lei and S. Zhang, “Flexible 3-d shape measurement using projector defocusing,” Opt. Lett. 34(20), 3080–3082 (2009).
[Crossref]

Li, H.

H. Nguyen, N. Dunne, H. Li, Y. Wang, and Z. Wang, “Real-time 3d shape measurement using 3lcd projection and deep machine learning,” Appl. Opt. 58(26), 7100–7109 (2019).
[Crossref]

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

Li, J.

Li, M.

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Li, R.

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

Li, X.

H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: A novel 3-d scanning technique for high-reflective surfaces,” Opt. Lasers Eng. 50(10), 1484–1493 (2012).
[Crossref]

Li, Y.

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Lin, B.

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

Liu, H. C.

Liu, K.

Liu, X.

Liu, Y.

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Lore, K. G.

K. G. Lore, A. Akintayo, and S. Sarkar, “Llnet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit. 61, 650–662 (2017).
[Crossref]

Loy, C. C.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

Lü, F.

Ma, J.

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

Matas, J.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

Meier, U.

D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), pp. 3642–3649.

Mishkin, D.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

Mykhailych, M.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

Nair, V.

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 807–814.

Nguyen, H.

Oliver, J.

Peng, X.

Philbin, J.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp 815–823.

Qian, K.

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Qingxiang, Wu

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Rastogi, P.

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: Whither we are?” Opt. Lasers Eng. 48(2), 133–140 (2010).
[Crossref]

Rathjen, C.

Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.

Romera, E.

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

Rongtai, Cai

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds. (Springer International Publishing, Cham, 2015), pp. 234–241.

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

Sarkar, S.

K. G. Lore, A. Akintayo, and S. Sarkar, “Llnet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit. 61, 650–662 (2017).
[Crossref]

Schmidhuber, J.

D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), pp. 3642–3649.

Schroff, F.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp 815–823.

Seah, H. S.

Shen, G.

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

Song, M.

F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

Srinivasan, V.

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

Su, X.

X. Su and Q. Zhang, “Dynamic 3-d shape measurement method: A review,” Opt. Lasers Eng. 48(2), 191–204 (2010).
[Crossref]

Q. Zhang and X. Su, “High-speed optical measurement for the drumhead vibration,” Opt. Express 13(8), 3110–3116 (2005).
[Crossref]

X. Su and W. Chen, “Fourier transform profilometry:: a review,” Opt. Lasers Eng. 35(5), 263–284 (2001).
[Crossref]

Sui, L.

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Sun, J.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.

Sun, T.

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

Sun, X.

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

Szegedy, C.

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of The 32nd International Conference on Machine Learning, (2015), 448–456.

Tang, X.

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

Tao, T.

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

Trusiak, M.

Wan, Y.

Wang, F.

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

Wang, H.

Wang, J.

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

Wang, X.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Wang, Y.

Wang, Z.

H. Nguyen, N. Dunne, H. Li, Y. Wang, and Z. Wang, “Real-time 3d shape measurement using 3lcd projection and deep machine learning,” Appl. Opt. 58(26), 7100–7109 (2019).
[Crossref]

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Weide, D. V. D.

Weng, J.

Wenzao, Shi

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Wu, H.

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Wu, Y.

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Wu, Z.

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

Xing, S.

Xu, N.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Yan, K.

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Yang, J.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Yang, T.

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

Yau, S.-T.

S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Opt. Eng. 48, 70660A (2009).
[Crossref]

Yi, J.

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Yin, W.

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

Yu, H.

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

Yu, J.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

Yu, S.

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Yu, X.

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Yu, Y.

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

Yuanhao, Wu

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Yue, H.

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Zhang, C.

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

Zhang, G.

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

Zhang, J.

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Zhang, L.

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

Zhang, Q.

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

X. Su and Q. Zhang, “Dynamic 3-d shape measurement method: A review,” Opt. Lasers Eng. 48(2), 191–204 (2010).
[Crossref]

Q. Zhang and X. Su, “High-speed optical measurement for the drumhead vibration,” Opt. Express 13(8), 3110–3116 (2005).
[Crossref]

Zhang, S.

S. Zhang, “High-speed 3d shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

S. Zhang, “Absolute phase retrieval methods for digital fringe projection profilometry: A review,” Opt. Lasers Eng. 107, 28–37 (2018).
[Crossref]

Y. An, J.-S. Hyun, and S. Zhang, “Pixel-wise absolute phase unwrapping using geometric constraints of structured light system,” Opt. Express 24(16), 18445–18459 (2016).
[Crossref]

S. Zhang, D. V. D. Weide, and J. Oliver, “Superfast phase-shifting method for 3-d shape measurement,” Opt. Express 18(9), 9684–9689 (2010).
[Crossref]

S. Zhang, “Recent progresses on real-time 3d shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
[Crossref]

S. Lei and S. Zhang, “Digital sinusoidal fringe pattern generation: Defocusing binary patterns vs focusing sinusoidal patterns,” Opt. Lasers Eng. 48(5), 561–569 (2010).
[Crossref]

S. Lei and S. Zhang, “Flexible 3-d shape measurement using projector defocusing,” Opt. Lett. 34(20), 3080–3082 (2009).
[Crossref]

S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Opt. Eng. 48, 70660A (2009).
[Crossref]

S. Zhang and P. S. Huang, “Phase error compensation for a 3-D shape measurement system based on the phase-shifting method,” Opt. Eng. 46, 60000E (2007).
[Crossref]

Zhang, X.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.

Zhang, Y.

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

Zhang, Z.

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

Zhao, H.

H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: A novel 3-d scanning technique for high-reflective surfaces,” Opt. Lasers Eng. 50(10), 1484–1493 (2012).
[Crossref]

Zheng, D.

Zhong, J.

Zhou, X.

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

Zichen, Wang

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

Zuo, C.

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
[Crossref]

Adv. Photonics (1)

S. Feng, Q. Chen, G. Gu, T. Tao, L. Zhang, Y. Hu, W. Yin, and C. Zuo, “Fringe pattern analysis using deep learning,” Adv. Photonics 1(02), 1–7 (2019).
[Crossref]

Appl. Opt. (8)

IEEE Trans. Intell. Transport. Syst. (1)

E. Romera, J. M. Álvarez, L. M. Bergasa, and R. Arroyo, “Erfnet: Efficient residual factorized convnet for real-time semantic segmentation,” IEEE Trans. Intell. Transport. Syst. 19(1), 263–272 (2018).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).
[Crossref]

J. Opt. Soc. Am. A (2)

Meas. Sci. Technol. (1)

S. Feng, L. Zhang, C. Zuo, T. Tao, Q. Chen, and G. Gu, “High dynamic range 3d measurements with fringe projection profilometry: a review,” Meas. Sci. Technol. 29(12), 122001 (2018).
[Crossref]

Opt. Commun. (2)

K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi, “Fringe pattern denoising based on deep learning,” Opt. Commun. 437, 148–152 (2019).
[Crossref]

S. Yu, J. Zhang, X. Yu, X. Sun, and H. Wu, “Unequal-period combination approach of gray code and phase-shifting for 3-d visual measurement,” Opt. Commun. 374, 97–106 (2016).
[Crossref]

Opt. Eng. (4)

S. Zhang and S.-T. Yau, “High dynamic range scanning technique,” Opt. Eng. 48, 70660A (2009).
[Crossref]

F. Chen, G. M. Brown, and M. Song, “Overview of 3-D shape measurement using optical methods,” Opt. Eng. 39(1), 10–22 (2000).
[Crossref]

S. Zhang and P. S. Huang, “Phase error compensation for a 3-D shape measurement system based on the phase-shifting method,” Opt. Eng. 46, 60000E (2007).
[Crossref]

Y. Wu, H. Yue, J. Yi, M. Li, and Y. Liu, “Phase error analysis and reduction in phase measuring deflectometry,” Opt. Eng. 54(6), 064103 (2015).
[Crossref]

Opt. Express (10)

S. Zhang, D. V. D. Weide, and J. Oliver, “Superfast phase-shifting method for 3-d shape measurement,” Opt. Express 18(9), 9684–9689 (2010).
[Crossref]

C. Zuo, Q. Chen, G. Gu, S. Feng, and F. Feng, “High-speed three-dimensional profilometry for multiple objects with complex shapes,” Opt. Express 20(17), 19493–19510 (2012).
[Crossref]

Q. Zhang and X. Su, “High-speed optical measurement for the drumhead vibration,” Opt. Express 13(8), 3110–3116 (2005).
[Crossref]

D. Zheng, F. Da, Q. Kemao, and H. S. Seah, “Phase-shifting profilometry combined with gray-code patterns projection: unwrapping error removal by an adaptive median filter,” Opt. Express 25(5), 4700–4713 (2017).
[Crossref]

Z. Wu, C. Zuo, W. Guo, T. Tao, and Q. Zhang, “High-speed three-dimensional shape measurement based on cyclic complementary gray-code light,” Opt. Express 27(2), 1283–1297 (2019).
[Crossref]

H. Yu, X. Chen, Z. Zhang, C. Zuo, Y. Zhang, D. Zheng, and J. Han, “Dynamic 3-d measurement based on fringe-to-fringe transformation using deep learning,” Opt. Express 28(7), 9405–9418 (2020).
[Crossref]

W. Yin, S. Feng, T. Tao, L. Huang, M. Trusiak, Q. Chen, and C. Zuo, “High-speed 3d shape measurement using the optimized composite fringe patterns and stereo-assisted structured light system,” Opt. Express 27(3), 2411–2431 (2019).
[Crossref]

K. Liu, Y. Wang, D. L. Lau, Q. Hao, and L. G. Hassebrook, “Dual-frequency pattern scheme for high-speed 3-d shape measurement,” Opt. Express 18(5), 5229–5244 (2010).
[Crossref]

Y. An, J.-S. Hyun, and S. Zhang, “Pixel-wise absolute phase unwrapping using geometric constraints of structured light system,” Opt. Express 24(16), 18445–18459 (2016).
[Crossref]

C. Chen, Y. Wan, and Y. Cao, “Instability of projection light source and real-time phase error correction method for phase-shifting profilometry,” Opt. Express 26(4), 4258–4270 (2018).
[Crossref]

Opt. Lasers Eng. (17)

X. He, D. Zheng, Q. Kemao, and G. Christopoulos, “Quaternary gray-code phase unwrapping for binary fringe projection profilometry,” Opt. Lasers Eng. 121, 358–368 (2019).
[Crossref]

S. Feng, C. Zuo, W. Yin, G. Gu, and Q. Chen, “Micro deep learning profilometry for high-speed 3d surface imaging,” Opt. Lasers Eng. 121, 416–427 (2019).
[Crossref]

S. Lei and S. Zhang, “Digital sinusoidal fringe pattern generation: Defocusing binary patterns vs focusing sinusoidal patterns,” Opt. Lasers Eng. 48(5), 561–569 (2010).
[Crossref]

H. Jiang, H. Zhao, and X. Li, “High dynamic range fringe acquisition: A novel 3-d scanning technique for high-reflective surfaces,” Opt. Lasers Eng. 50(10), 1484–1493 (2012).
[Crossref]

S. Feng, Y. Zhang, Q. Chen, C. Zuo, R. Li, and G. Shen, “General solution for high dynamic range three-dimensional shape measurement using the fringe projection technique,” Opt. Lasers Eng. 59, 56–71 (2014).
[Crossref]

T. Yang, G. Zhang, H. Li, Z. Zhang, and X. Zhou, “Theoretical proof of parameter optimization for sinusoidal fringe projection profilometry,” Opt. Lasers Eng. 123, 37–44 (2019).
[Crossref]

C. Zuo, S. Feng, L. Huang, T. Tao, W. Yin, and Q. Chen, “Phase shifting algorithms for fringe projection profilometry: A review,” Opt. Lasers Eng. 109, 23–59 (2018).
[Crossref]

X. Su and Q. Zhang, “Dynamic 3-d shape measurement method: A review,” Opt. Lasers Eng. 48(2), 191–204 (2010).
[Crossref]

X. Su and W. Chen, “Fourier transform profilometry:: a review,” Opt. Lasers Eng. 35(5), 263–284 (2001).
[Crossref]

S. Zhang, “High-speed 3d shape measurement with structured light methods: A review,” Opt. Lasers Eng. 106, 119–131 (2018).
[Crossref]

B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li, “Optical fringe patterns filtering based on multi-stage convolution neural network,” Opt. Lasers Eng. 126, 105853 (2020).
[Crossref]

S. S. Gorthi and P. Rastogi, “Fringe projection techniques: Whither we are?” Opt. Lasers Eng. 48(2), 133–140 (2010).
[Crossref]

D. Zheng, Q. Kemao, J. Han, J. Wang, H. Yu, and L. Bai, “High-speed phase-shifting profilometry under fluorescent light,” Opt. Lasers Eng. 128, 106033 (2020).
[Crossref]

K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao, “Wrapped phase denoising using convolutional neural networks,” Opt. Lasers Eng. 128, 105999 (2020).
[Crossref]

S. Zhang, “Recent progresses on real-time 3d shape measurement using digital fringe projection techniques,” Opt. Lasers Eng. 48(2), 149–158 (2010).
[Crossref]

W. Yin, C. Zuo, S. Feng, T. Tao, Y. Hu, L. Huang, J. Ma, and Q. Chen, “High-speed three-dimensional shape measurement using geometry-constraint-based number-theoretical phase unwrapping,” Opt. Lasers Eng. 115, 21–31 (2019).
[Crossref]

S. Zhang, “Absolute phase retrieval methods for digital fringe projection profilometry: A review,” Opt. Lasers Eng. 107, 28–37 (2018).
[Crossref]

Opt. Lett. (2)

Pattern Recognit. (1)

K. G. Lore, A. Akintayo, and S. Sarkar, “Llnet: A deep autoencoder approach to natural low-light image enhancement,” Pattern Recognit. 61, 650–662 (2017).
[Crossref]

Photonics Res. (1)

Z. Wu, W. Guo, Y. Li, Y. Liu, and Q. Zhang, “High-speed and high-efficiency three-dimensional shape measurement based on gray-coded light,” Photonics Res. 8(6), 819–829 (2020).
[Crossref]

Other (10)

V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning (Omnipress, Madison, WI, USA, 2010), ICML’10, pp. 807–814.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 (2014).

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, eds. (Springer International Publishing, Cham, 2015), pp. 234–241.

J. Yu, Y. Fan, J. Yang, N. Xu, Z. Wang, X. Wang, and T. S. Huang, “Wide activation for efficient and accurate image super-resolution,” arXiv preprint arXiv:1808.08718 (2018).

S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of The 32nd International Conference on Machine Learning, (2015), 448–456.

Cai Rongtai, Wu Qingxiang, Shi Wenzao, Sun Honghai, Wu Yuanhao, and Wang Zichen, “Ccd performance model and noise control,” in 2011 International Conference on Image Analysis and Signal Processing, (2011), pp. 389–394.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas, “Deblurgan: Blind motion deblurring using conditional adversarial networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2018), pp. 8183–8192.

F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp 815–823.

D. Ciregan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, (2012), pp. 3642–3649.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778.

Supplementary Material (1)

NameDescription
» Visualization 1       A dynamic measurement scene (i.e., fringes captured under different exposures and their resulting 3-D shapes) is provided in Visualization 1.

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Figures (9)

Fig. 1.
Fig. 1. The phase error: (a) for different fringe modulations, and (b) phase steps.
Fig. 2.
Fig. 2. Schematic diagram of the proposed FMEM.
Fig. 3.
Fig. 3. Network structure of the designed FMENet.
Fig. 4.
Fig. 4. Experimental results of FMENet. (a) The inputted fringes by FMENet. (b) The outputted fringes by FMENet. (c) The ground-truth. (d) The intensity of the 158th row of first phase-shifted fringes.
Fig. 5.
Fig. 5. Phase error distributions of the scene captured under the camera exposure of 250 us. (a) The phase error. (b) The details of the object’s eyes and mouth.
Fig. 6.
Fig. 6. Phase error distributions of the scene captured under the camera exposure of 750 us. (a) The phase error. (b) The details of the object’s eyes and mouth.
Fig. 7.
Fig. 7. 3-D shapes of the scene captured under the camera exposure of 250 us. (a) The 3-D shapes. (b) The details of the object’s eyes. (c) The details of the object’s mouth. (d) The measurement error of the 126th row.
Fig. 8.
Fig. 8. 3-D shapes of the scene captured under the camera exposure of 750 us. (a) The 3-D shapes. (b) The details of the object’s eyes. (c) The details of the object’s mouth. (d) The measurement error of the 126th row.
Fig. 9.
Fig. 9. 3-D reconstruction for the scene with un-uniform reflectivity. (a)The fringes. (b) The 3-D shapes.

Tables (2)

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Table 1. Fringe modulations under seven relatively small camera exposures.

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Table 2. Phase error of different methods.

Equations (6)

Equations on this page are rendered with MathJax. Learn more.

I n ( x , y ) = a ( x , y ) + Δ a n ( x , y ) + b ( x , y ) cos [ φ ( x , y ) δ n ] , n = 1 , 2 , 3 , , N ,
b ( x , y ) = s t e α ( x , y ) b p ( x , y ) ,
b = 2 N ( n = 1 N I n sin δ n ) 2 + ( n = 1 N I n cos δ n ) 2 .
φ = tan 1 n = 1 N I n sin δ n n = 1 N I n cos δ n .
Δ φ = 2 N b n = 1 N sin ( δ n φ ) Δ a n ,
L o s s ( θ 1 ) = 1 m n = 1 3 I n o u t I n g t 2 ,