Phase unwrapping in optical metrology via denoised and convolutional segmentation networks
The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are...
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| Published in | Optics express Vol. 27; no. 10; p. 14903 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
13.05.2019
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| Online Access | Get full text |
| ISSN | 1094-4087 1094-4087 |
| DOI | 10.1364/OE.27.014903 |
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| Abstract | The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system. |
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| AbstractList | The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system.The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system. The interferometry technique is commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 2π ambiguity. To remove the ambiguity and obtain the correct phase, phase unwrapping is essential. Conventional phase unwrapping approaches are time-consuming and noise sensitive. To address those issues, we propose a new approach, where we transfer the task of phase unwrapping into a multi-class classification problem and introduce an efficient segmentation network to identify classes. Moreover, a noise-to-noise denoised network is integrated to preprocess noisy wrapped phase. We have demonstrated the proposed method with simulated data and in a real interferometric system. |
| Author | Shao, Jianbo Zhang, Junchao Luo, Haibo Tian, Xiaobo Liang, Rongguang |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31163931$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1364/AO.56.007079 10.1109/LGRS.2016.2535159 10.1364/OE.26.018279 10.1364/JOSAA.11.000107 10.1029/RS023i004p00713 10.1109/TPAMI.2016.2644615 10.1109/LGRS.2010.2076362 10.1016/j.optcom.2013.07.013 10.1364/AO.50.006214 10.1364/AO.53.005439 10.1117/1.OE.53.2.024102 10.1364/AO.55.002418 10.1364/JOSAA.14.002692 10.1109/LSP.2018.2879184 |
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| References | Zhong (oe-27-10-14903-R4) 2011; 8 Xu (oe-27-10-14903-R6) 2016; 13 Badrinarayanan (oe-27-10-14903-R16) 2017; 39 Nair (oe-27-10-14903-R18) 2010 Juarez-Salazar (oe-27-10-14903-R8) 2014; 53 Martinez-Carranza (oe-27-10-14903-R11) 2017; 56 Goldstein (oe-27-10-14903-R1) 1988; 23 Tian (oe-27-10-14903-R27) 2018; 26 Sawaf (oe-27-10-14903-R23) 2014; 53 Flynn (oe-27-10-14903-R2) 1996; 4 Zhao (oe-27-10-14903-R3) 2011; 50 Schwartzkopf (oe-27-10-14903-R12) 2000 Pandey (oe-27-10-14903-R10) 2016; 55 Zuo (oe-27-10-14903-R9) 2013; 309 Ioffe (oe-27-10-14903-R17) 2015 He (oe-27-10-14903-R21) 2015 Ghiglia (oe-27-10-14903-R7) 1994; 11 Spoorthi (oe-27-10-14903-R15) 2019; 26 Flynn (oe-27-10-14903-R5) 1997; 14 Olaf (oe-27-10-14903-R25) 2015 |
| References_xml | – volume: 56 start-page: 7079 year: 2017 ident: oe-27-10-14903-R11 publication-title: Appl. Opt. doi: 10.1364/AO.56.007079 – volume: 13 start-page: 666 year: 2016 ident: oe-27-10-14903-R6 publication-title: IEEE Geosci. Remote. Sens. Lett. doi: 10.1109/LGRS.2016.2535159 – volume: 26 start-page: 18279 year: 2018 ident: oe-27-10-14903-R27 publication-title: Opt. Express doi: 10.1364/OE.26.018279 – volume: 11 start-page: 107 year: 1994 ident: oe-27-10-14903-R7 publication-title: J. Opt. Soc. Am. A doi: 10.1364/JOSAA.11.000107 – start-page: 234 volume-title: Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention year: 2015 ident: oe-27-10-14903-R25 article-title: U-net: Convolutional networks for biomedical image segmentation – volume: 23 start-page: 713 year: 1988 ident: oe-27-10-14903-R1 publication-title: Radio Sci. doi: 10.1029/RS023i004p00713 – volume: 4 start-page: 2057 volume-title: Geoscience and Remote Sensing Symposium year: 1996 ident: oe-27-10-14903-R2 article-title: Consistent 2-d phase unwrapping guided by a quality map – volume: 39 start-page: 2481 year: 2017 ident: oe-27-10-14903-R16 publication-title: IEEE Transactions on Pattern Analysis Mach. Intell. doi: 10.1109/TPAMI.2016.2644615 – volume: 8 start-page: 364 year: 2011 ident: oe-27-10-14903-R4 publication-title: IEEE Geosci. Remote. Sens. Lett. doi: 10.1109/LGRS.2010.2076362 – volume: 309 start-page: 221 year: 2013 ident: oe-27-10-14903-R9 publication-title: Opt. Commun. doi: 10.1016/j.optcom.2013.07.013 – volume: 50 start-page: 6214 year: 2011 ident: oe-27-10-14903-R3 publication-title: Appl. Opt. doi: 10.1364/AO.50.006214 – volume: 53 start-page: 5439 year: 2014 ident: oe-27-10-14903-R23 publication-title: Appl. Opt. doi: 10.1364/AO.53.005439 – start-page: 448 volume-title: Proceedings of International Conference on Machine Learning year: 2015 ident: oe-27-10-14903-R17 article-title: Batch normalization: Accelerating deep network training by reducing internal covariate shift – volume: 53 start-page: 024102 year: 2014 ident: oe-27-10-14903-R8 publication-title: Opt. Eng. doi: 10.1117/1.OE.53.2.024102 – volume: 55 start-page: 2418 year: 2016 ident: oe-27-10-14903-R10 publication-title: Appl. Opt. doi: 10.1364/AO.55.002418 – start-page: 274 volume-title: Proceedings of IEEE Conference on Image Analysis and Interpretation year: 2000 ident: oe-27-10-14903-R12 article-title: Two-dimensional phase unwrapping using neural networks – volume: 14 start-page: 2692 year: 1997 ident: oe-27-10-14903-R5 publication-title: J. Opt. Soc. Am. A doi: 10.1364/JOSAA.14.002692 – start-page: 1026 volume-title: Proceedings of IEEE International Conference on Computer Vision year: 2015 ident: oe-27-10-14903-R21 article-title: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification – start-page: 807 volume-title: Proceedings of the 27th international conference on machine learning year: 2010 ident: oe-27-10-14903-R18 article-title: Rectified linear units improve restricted boltzmann machines – volume: 26 start-page: 54 year: 2019 ident: oe-27-10-14903-R15 publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2018.2879184 |
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