Phase unwrapping in optical metrology via denoised and convolutional segmentation networks
AffiliationUniv Arizona, Coll Opt Sci
MetadataShow full item record
PublisherOPTICAL SOC AMER
CitationZhang, J., Tian, X., Shao, J., Luo, H., & Liang, R. (2019). Phase unwrapping in optical metrology via denoised and convolutional segmentation networks. Optics express, 27(10), 14903-14912.
Rights© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractThe interferometry technique is corn commonly used to obtain the phase information of an object in optical metrology. The obtained wrapped phase is subject to a 27 pi 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.
NoteOpen access journal
VersionFinal published version
SponsorsChina Scholarship Council (CSC) ; National Science Foundation (NSF) 
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