| 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 | |
https://doi.org/10.1051/epjconf/202023806009
Fringe Pattern Denoising using U-Net based neural network
1 Departamento de Fisica Aplicada. Universidade de Santiago de Compostela. Spain
2 RNASA-IMEDIR, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
3 CITIC, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
4 CITEEC, Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
* Corresponding author: jose.crespo@me.com
Published online: 20 August 2020
Fringe visibility and noise removal, are key success factors in interferometric techniques, where novel deep learning techniques can be applied. We test the use U-Net deep convolutional network applied to the obtained interference images, trained with an ad-hoc generated image dataset with complex fringe patterns, computed using high order Zernike polynomials.
© The Authors, published by EDP Sciences, 2020
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