Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning
Fourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/423 |
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| author | Xuhui Zhou Haiping Tong Er Ouyang Lin Zhao Hui Fang |
| author_facet | Xuhui Zhou Haiping Tong Er Ouyang Lin Zhao Hui Fang |
| author_sort | Xuhui Zhou |
| collection | DOAJ |
| description | Fourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors such as noise, optical aberration, and phase wrapping. In this work, we propose a semi-supervised Fourier ptychographic transformer network (SFPT) for improved image reconstruction, which employs a two-stage training approach to enhance the image quality. First, self-supervised learning guided by low-resolution amplitudes and Zernike modes is utilized to recover pupil function. Second, a supervised learning framework with augmented training datasets is applied to further refine reconstruction quality. Moreover, the unwrapped phase is recovered by adjusting the phase distribution range in the augmented training datasets. The effectiveness of the proposed method is validated by using both the simulation and experimental data. This deep-learning-based method has potential applications for imaging thicker biology samples. |
| format | Article |
| id | doaj-art-d760127ce6e34fcdaff8b6eafb6d6397 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d760127ce6e34fcdaff8b6eafb6d63972025-01-10T13:15:30ZengMDPI AGApplied Sciences2076-34172025-01-0115142310.3390/app15010423Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised LearningXuhui Zhou0Haiping Tong1Er Ouyang2Lin Zhao3Hui Fang4Nanophotonics Research Center, Institute of Microscale Optoelectronics & Shenzhen Key Laboratory of Microscale Optical Information Technology, Shenzhen University, Shenzhen 518060, ChinaNanophotonics Research Center, Institute of Microscale Optoelectronics & Shenzhen Key Laboratory of Microscale Optical Information Technology, Shenzhen University, Shenzhen 518060, ChinaNanophotonics Research Center, Institute of Microscale Optoelectronics & Shenzhen Key Laboratory of Microscale Optical Information Technology, Shenzhen University, Shenzhen 518060, ChinaSchool of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414000, ChinaNanophotonics Research Center, Institute of Microscale Optoelectronics & Shenzhen Key Laboratory of Microscale Optical Information Technology, Shenzhen University, Shenzhen 518060, ChinaFourier ptychographic microscopy (FPM) has recently emerged as an important non-invasive imaging technique which is capable of simultaneously achieving high resolution, wide field of view, and quantitative phase imaging. However, FPM still faces challenges in the image reconstruction due to factors such as noise, optical aberration, and phase wrapping. In this work, we propose a semi-supervised Fourier ptychographic transformer network (SFPT) for improved image reconstruction, which employs a two-stage training approach to enhance the image quality. First, self-supervised learning guided by low-resolution amplitudes and Zernike modes is utilized to recover pupil function. Second, a supervised learning framework with augmented training datasets is applied to further refine reconstruction quality. Moreover, the unwrapped phase is recovered by adjusting the phase distribution range in the augmented training datasets. The effectiveness of the proposed method is validated by using both the simulation and experimental data. This deep-learning-based method has potential applications for imaging thicker biology samples.https://www.mdpi.com/2076-3417/15/1/423FPMsemi-supervised learningunwrapped phasetransformerdeep learning |
| spellingShingle | Xuhui Zhou Haiping Tong Er Ouyang Lin Zhao Hui Fang Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning Applied Sciences FPM semi-supervised learning unwrapped phase transformer deep learning |
| title | Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning |
| title_full | Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning |
| title_fullStr | Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning |
| title_full_unstemmed | Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning |
| title_short | Fourier Ptychographic Microscopy with Optical Aberration Correction and Phase Unwrapping Based on Semi-Supervised Learning |
| title_sort | fourier ptychographic microscopy with optical aberration correction and phase unwrapping based on semi supervised learning |
| topic | FPM semi-supervised learning unwrapped phase transformer deep learning |
| url | https://www.mdpi.com/2076-3417/15/1/423 |
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