Macroscopic Fourier Ptychographic Imaging Based on Deep Learning
Fourier Ptychography (FP) is a powerful computational imaging technique that enables high-resolution, wide-field imaging by synthesizing apertures and leveraging coherent diffraction. However, the application of FP in long-distance imaging has been limited due to challenges such as noise and optical...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/12/2/170 |
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| author | Junyuan Liu Wei Sun Fangxun Wu Haoming Shan Xiangsheng Xie |
| author_facet | Junyuan Liu Wei Sun Fangxun Wu Haoming Shan Xiangsheng Xie |
| author_sort | Junyuan Liu |
| collection | DOAJ |
| description | Fourier Ptychography (FP) is a powerful computational imaging technique that enables high-resolution, wide-field imaging by synthesizing apertures and leveraging coherent diffraction. However, the application of FP in long-distance imaging has been limited due to challenges such as noise and optical aberrations. This study introduces deep learning methods following macroscopic FP to further enhance image quality. Specifically, we employ super-resolution convolutional neural networks and very deep super-resolution, incorporating residual learning and residual neural network architectures to optimize network performance. These techniques significantly improve the resolution and clarity of FP images. Experiments with real-world film samples demonstrate the effectiveness of the proposed methods in practical applications. This research highlights the potential of deep learning to advance computational imaging techniques like FP, paving the way for improved long-distance imaging capabilities. |
| format | Article |
| id | doaj-art-d468b200230842ebaf982e6fb6e6658a |
| institution | DOAJ |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-d468b200230842ebaf982e6fb6e6658a2025-08-20T02:44:32ZengMDPI AGPhotonics2304-67322025-02-0112217010.3390/photonics12020170Macroscopic Fourier Ptychographic Imaging Based on Deep LearningJunyuan Liu0Wei Sun1Fangxun Wu2Haoming Shan3Xiangsheng Xie4Department of Physics, College of Science, Shantou University, Shantou 515063, ChinaDepartment of Physics, College of Science, Shantou University, Shantou 515063, ChinaDepartment of Physics, College of Science, Shantou University, Shantou 515063, ChinaDepartment of Physics, College of Science, Shantou University, Shantou 515063, ChinaDepartment of Physics, College of Science, Shantou University, Shantou 515063, ChinaFourier Ptychography (FP) is a powerful computational imaging technique that enables high-resolution, wide-field imaging by synthesizing apertures and leveraging coherent diffraction. However, the application of FP in long-distance imaging has been limited due to challenges such as noise and optical aberrations. This study introduces deep learning methods following macroscopic FP to further enhance image quality. Specifically, we employ super-resolution convolutional neural networks and very deep super-resolution, incorporating residual learning and residual neural network architectures to optimize network performance. These techniques significantly improve the resolution and clarity of FP images. Experiments with real-world film samples demonstrate the effectiveness of the proposed methods in practical applications. This research highlights the potential of deep learning to advance computational imaging techniques like FP, paving the way for improved long-distance imaging capabilities.https://www.mdpi.com/2304-6732/12/2/170FPSRCNNVDSRresidual learningResNet |
| spellingShingle | Junyuan Liu Wei Sun Fangxun Wu Haoming Shan Xiangsheng Xie Macroscopic Fourier Ptychographic Imaging Based on Deep Learning Photonics FP SRCNN VDSR residual learning ResNet |
| title | Macroscopic Fourier Ptychographic Imaging Based on Deep Learning |
| title_full | Macroscopic Fourier Ptychographic Imaging Based on Deep Learning |
| title_fullStr | Macroscopic Fourier Ptychographic Imaging Based on Deep Learning |
| title_full_unstemmed | Macroscopic Fourier Ptychographic Imaging Based on Deep Learning |
| title_short | Macroscopic Fourier Ptychographic Imaging Based on Deep Learning |
| title_sort | macroscopic fourier ptychographic imaging based on deep learning |
| topic | FP SRCNN VDSR residual learning ResNet |
| url | https://www.mdpi.com/2304-6732/12/2/170 |
| work_keys_str_mv | AT junyuanliu macroscopicfourierptychographicimagingbasedondeeplearning AT weisun macroscopicfourierptychographicimagingbasedondeeplearning AT fangxunwu macroscopicfourierptychographicimagingbasedondeeplearning AT haomingshan macroscopicfourierptychographicimagingbasedondeeplearning AT xiangshengxie macroscopicfourierptychographicimagingbasedondeeplearning |