Physics-informed neural network for phase imaging based on transport of intensity equation

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dc.contributor.authorWu, Xiaofengen
dc.contributor.authorWu, Zilingen
dc.contributor.authorShanmugavel, Sibi Chakravarthyen
dc.contributor.authorYu, Hang Z.en
dc.contributor.authorZhu, Yunhuien
dc.date.accessioned2023-04-19T14:59:02Zen
dc.date.available2023-04-19T14:59:02Zen
dc.date.issued2022-11en
dc.description.abstractNon-interferometric quantitative phase imaging based on Transport of Intensity Equation (T1E) has been widely used in bio-medical imaging. However, analytic TIE phase retrieval is prone to low-spatial frequency noise amplification, which is caused by the iliposedness of inversion at the origin of the spectrum. There are also retrieval ambiguities resulting from the lack of sensitivity to the curl component of the Poynting vector occurring with strong absorption. Here, we establish a physics-informed neural network (PINN) to address these issues, by integrating the forward and inverse physics models into a cascaded deep neural network. We demonstrate that the proposed PINN is efficiently trained using a small set of sample data, enabling the conversion of noise-corrupted 2-shot TIE phase retrievals to high quality phase images under partially coherent LED illumination. The efficacy of the proposed approach is demonstrated by both simulation using a standard image database and experiment using human buccal epitehlial cells. In particular, high image quality (SSIM = 0.919) is achieved experimentally using a reduced size of labeled data (140 image pairs). We discuss the robustness of the proposed approach against insufficient training data, and demonstrate that the parallel architecture of PINN is efficient for transfer learning.en
dc.description.notesNational Science Foundation (QMR-1825646).en
dc.description.sponsorshipNational Science Foundation [QMR-1825646]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1364/OE.462844en
dc.identifier.issue24en
dc.identifier.pmid36523038en
dc.identifier.urihttp://hdl.handle.net/10919/114566en
dc.identifier.volume30en
dc.language.isoenen
dc.publisherOptica Publishing Groupen
dc.subjectStructured illumination transporten
dc.subjecthybrid uniformen
dc.subjectretrievalen
dc.subjectcontrasten
dc.subjectmicroscopyen
dc.subjectreconstructionen
dc.subjectregularizationen
dc.subjectexpertsen
dc.subjectfieldsen
dc.titlePhysics-informed neural network for phase imaging based on transport of intensity equationen
dc.title.serialOptics Expressen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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