Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/6/3590 |
| _version_ | 1797613685531738112 |
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| author | Xiaoli Wang Yan Piao Yuanshang Jin Jie Li Zechuan Lin Jie Cui Tingfa Xu |
| author_facet | Xiaoli Wang Yan Piao Yuanshang Jin Jie Li Zechuan Lin Jie Cui Tingfa Xu |
| author_sort | Xiaoli Wang |
| collection | DOAJ |
| description | Fourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is introduced into the reconstruction method for the first time. The input of the SwinIR physical model is modified to a two-channel input, and a data set is established to train the network. Finally, the results of high-quality Fourier stack microscopic reconstruction are realized. The SwinIR network is used as the physical model, and the network hyperparameters and processes such as the loss function and optimizer of the custom network are reconstructed. The experimental results show that by using multiple different types of data sets, the two evaluation index values of the proposed method perform best, and the image reconstruction quality is the best after model training. Two different evaluation indexes are used to quantitatively analyze the reconstruction results through numerical results. The reconstruction results of the fine-tuning data set with some real captured images are qualitatively analyzed from the visual effect. The results show that the proposed method is effective, the network model is stable and feasible, the image reconstruction is realized in a short time, and the reconstruction effect is good. |
| first_indexed | 2024-03-11T06:58:19Z |
| format | Article |
| id | doaj.art-2ad71593521b48509993390c2c152f33 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:58:19Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2ad71593521b48509993390c2c152f332023-11-17T09:23:56ZengMDPI AGApplied Sciences2076-34172023-03-01136359010.3390/app13063590Fourier Ptychographic Reconstruction Method of Self-Training Physical ModelXiaoli Wang0Yan Piao1Yuanshang Jin2Jie Li3Zechuan Lin4Jie Cui5Tingfa Xu6Information and Communication Engineering, Electronics Information Engineering College, Changchun University of Science and Technology, Changchun 130022, ChinaInformation and Communication Engineering, Electronics Information Engineering College, Changchun University of Science and Technology, Changchun 130022, ChinaElectronics Information Engineering College, Changchun University, Changchun 130022, ChinaElectronics Information Engineering College, Changchun University, Changchun 130022, ChinaElectronics Information Engineering College, Changchun University, Changchun 130022, ChinaElectronics Information Engineering College, Changchun University, Changchun 130022, ChinaSchool of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, ChinaFourier ptychographic microscopy is a new microscopic computational imaging technology. A series of low-resolution intensity images are collected by a Fourier ptychographic microscopy system, and high-resolution intensity and phase images are reconstructed from the collected low-resolution images by a reconstruction algorithm. It is a kind of microscopy that can achieve both a large field of view and high resolution. Here in this article, a Fourier ptychographic reconstruction method applied to a self-training physical model is proposed. The SwinIR network in the field of super-resolution is introduced into the reconstruction method for the first time. The input of the SwinIR physical model is modified to a two-channel input, and a data set is established to train the network. Finally, the results of high-quality Fourier stack microscopic reconstruction are realized. The SwinIR network is used as the physical model, and the network hyperparameters and processes such as the loss function and optimizer of the custom network are reconstructed. The experimental results show that by using multiple different types of data sets, the two evaluation index values of the proposed method perform best, and the image reconstruction quality is the best after model training. Two different evaluation indexes are used to quantitatively analyze the reconstruction results through numerical results. The reconstruction results of the fine-tuning data set with some real captured images are qualitatively analyzed from the visual effect. The results show that the proposed method is effective, the network model is stable and feasible, the image reconstruction is realized in a short time, and the reconstruction effect is good.https://www.mdpi.com/2076-3417/13/6/3590fourier ptychographic microscopyreconstructionSwinIRself-training physical model |
| spellingShingle | Xiaoli Wang Yan Piao Yuanshang Jin Jie Li Zechuan Lin Jie Cui Tingfa Xu Fourier Ptychographic Reconstruction Method of Self-Training Physical Model Applied Sciences fourier ptychographic microscopy reconstruction SwinIR self-training physical model |
| title | Fourier Ptychographic Reconstruction Method of Self-Training Physical Model |
| title_full | Fourier Ptychographic Reconstruction Method of Self-Training Physical Model |
| title_fullStr | Fourier Ptychographic Reconstruction Method of Self-Training Physical Model |
| title_full_unstemmed | Fourier Ptychographic Reconstruction Method of Self-Training Physical Model |
| title_short | Fourier Ptychographic Reconstruction Method of Self-Training Physical Model |
| title_sort | fourier ptychographic reconstruction method of self training physical model |
| topic | fourier ptychographic microscopy reconstruction SwinIR self-training physical model |
| url | https://www.mdpi.com/2076-3417/13/6/3590 |
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