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.

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.