| Autor: |
Zhuoshi Li, Jiasong Sun, Yao Fan, Yanbo Jin, Qian Shen, Maciej Trusiak, Maria Cywińska, Peng Gao, Qian Chen, Chao Zuo |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Opto-Electronic Science, Vol 2, Iss 4, Pp 1-11 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2097-0382 |
| DOI: |
10.29026/oes.2023.220023 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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