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• W4298145917 endingPage "43398" @default.
• W4298145917 startingPage "43398" @default.
• W4298145917 abstract "Non-interferometric quantitative phase imaging based on Transport of Intensity Equation (TIE) 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 illposedness 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." @default.
• W4298145917 created "2022-10-01" @default.
• W4298145917 creator A5017705206 @default.
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• W4298145917 creator A5068024307 @default.
• W4298145917 date "2022-11-10" @default.
• W4298145917 modified "2023-09-30" @default.
• W4298145917 title "Physics-informed neural network for phase imaging based on transport of intensity equation" @default.
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• W4298145917 doi "https://doi.org/10.1364/oe.462844" @default.
• W4298145917 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36523038" @default.
• W4298145917 hasPublicationYear "2022" @default.
• W4298145917 type Work @default.
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