Fringe Pattern Denoising using U-Net based neural network

J. M. Crespo; V. Moreno; Juan Ramón Rabuñal; Alejandro Pazos; Monica Canabal Carbia

doi:10.1051/epjconf/202023806009

All issues

Volume 238 (2020)

EPJ Web Conf., 238 (2020) 06009

References

Open Access

Issue		EPJ Web Conf. Volume 238, 2020 EOS Annual Meeting (EOSAM 2020)


Article Number		06009
Number of page(s)		2
Section		Topical Meeting (TOM) 6- Frontiers in Optical Metrology
DOI		https://doi.org/10.1051/epjconf/202023806009
Published online		20 August 2020

Q. Kemao, Two-dimensional windowed fourier transform for fringe pattern analysis: Principles, applications and implementations, Opt. & Las. Eng., vol. 45, no. 12, pp. 1146–1192, 2007 [Google Scholar]
H. Wang and Q. Kemao, Comparative analysis on some spatial domain filters for fringe pattern denoising, Appl. Opt. 50 (12), [Google Scholar]
Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. 2015. [Google Scholar]
Heinrich, M.P., Stille, M. and Buzug, T.M. Residual U-net convolutional neural network architecture for low-dose CT denoising. Current Directions in Biomedical Engineering, 4(1),pp.297-300. [Google Scholar]
Lattari, Francesco, et al. Deep learning for SAR image despeckling. Remote Sensing 11.13, 2019 [Google Scholar]
Ketao Yan, Yingjie Yu, Tao Sun, et al. Wrapped phase denoising using convolutional neural networks. Optics and Lasers in Engineering, 2020 [Google Scholar]
Diederik P. Kigma, Jimmy BA. Adam: A method for stochastic optimization. 2014. [Google Scholar]
Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 2004 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.

[1] Q. Kemao, Two-dimensional windowed fourier transform for fringe pattern analysis: Principles, applications and implementations, Opt. & Las. Eng., vol. 45, no. 12, pp. 1146–1192, 2007 [Google Scholar]

[2] H. Wang and Q. Kemao, Comparative analysis on some spatial domain filters for fringe pattern denoising, Appl. Opt. 50 (12), [Google Scholar]

[3] Ronneberger, O., Fischer, P., & Brox, T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. 2015. [Google Scholar]

[4] Heinrich, M.P., Stille, M. and Buzug, T.M. Residual U-net convolutional neural network architecture for low-dose CT denoising. Current Directions in Biomedical Engineering, 4(1),pp.297-300. [Google Scholar]

[5] Lattari, Francesco, et al. Deep learning for SAR image despeckling. Remote Sensing 11.13, 2019 [Google Scholar]

[6] Ketao Yan, Yingjie Yu, Tao Sun, et al. Wrapped phase denoising using convolutional neural networks. Optics and Lasers in Engineering, 2020 [Google Scholar]

[7] Diederik P. Kigma, Jimmy BA. Adam: A method for stochastic optimization. 2014. [Google Scholar]

[8] Zhou Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing. 2004 [Google Scholar]