Li ZS, Sun JS, Fan Y, Jin YB, Shen Q et al. Deep learning assisted variational Hilbert quantitative phase imaging. Opto-Electron Sci 2, 220023 (2023). doi: 10.29026/oes.2023.220023
Citation: Li ZS, Sun JS, Fan Y, Jin YB, Shen Q et al. Deep learning assisted variational Hilbert quantitative phase imaging. Opto-Electron Sci 2, 220023 (2023). doi: 10.29026/oes.2023.220023

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Deep learning assisted variational Hilbert quantitative phase imaging

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  • We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively low-carrier frequency holograms—deep learning assisted variational Hilbert quantitative phase imaging (DL-VHQPI). The method, incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation, reliably and robustly recovers the quantitative phase information of the test objects. It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system. Compared to the conventional end-to-end networks (without a physical model), the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization. The DL-VHQPI is quantitatively studied by numerical simulation. The live-cell experiment is designed to demonstrate the method's practicality in biological research. The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
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