A Fringe Phase Extraction Method Based on Neural Network
In optical metrology, the output is usually in the form of a fringe pattern, from which a phase map can be generated and phase information can be converted into the desired parameters. This paper proposes an end-to-end method of fringe phase extraction based on the neural network. This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods. The results of simulation and experimental fringe patterns verify the accuracy and the robustness of this method. While it yields the same accuracy, the proposed method features easier operation and a simpler principle than the traditional phase-shifting method and has a faster speed than wavelet transform method.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 21(2021), 5 vom: 28. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hu, Wenxin [VerfasserIn] |
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| Links: |
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| Themen: |
Fringe pattern |
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| Anmerkungen: |
Date Completed 11.03.2021 Date Revised 17.03.2021 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s21051664 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM32228242X |
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| 100 | 1 | |a Hu, Wenxin |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Fringe Phase Extraction Method Based on Neural Network |
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| 500 | |a Date Completed 11.03.2021 | ||
| 500 | |a Date Revised 17.03.2021 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a In optical metrology, the output is usually in the form of a fringe pattern, from which a phase map can be generated and phase information can be converted into the desired parameters. This paper proposes an end-to-end method of fringe phase extraction based on the neural network. This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods. The results of simulation and experimental fringe patterns verify the accuracy and the robustness of this method. While it yields the same accuracy, the proposed method features easier operation and a simpler principle than the traditional phase-shifting method and has a faster speed than wavelet transform method | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a U-net neural network | |
| 650 | 4 | |a fringe pattern | |
| 650 | 4 | |a phase extraction | |
| 650 | 4 | |a warped phase map | |
| 700 | 1 | |a Miao, Hong |e verfasserin |4 aut | |
| 700 | 1 | |a Yan, Keyu |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Yu |e verfasserin |4 aut | |
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