# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 4 Experimental Results and Analysis
### Comparison and Analysis of Reconstruction Methods under Different Noise  
#### 4.3.1 Comparison Results and Analysis of Reconstruction Methods under the SAME Noise  
The true value image used in this experiment is shown in Figure 16, Including the first behavior amplitude image and the second behavior phase image. The images used in this paper are 192 \(\times\) 192.  
Considering the process of real reconstruction of the Fourier ptychographic microscopy imaging system, the noise effect that may occur in the actual acquisition process is simulated, because the noise source in the actual acquisition is mainly from the intensity fluctuation of the LED component, and Gaussian noise is caused by sensor noise caused by different brightness light sources in the process of image acquisition. Therefore, in the subsequent experiments, we use Gaussian noise with different standard deviations as the main interference condition of our experiment, based on which we verify the robustness and anti-interference of the experimental method in this paper.  
Under the data set with Gaussian noise, the Fourier ptychographic microscopy reconstruction results are obtained by using the STPM-FPM method in this paper. The traditional phase recovery G-S method, A-S method, [PERSON] et al.'s method based on neural network reconstruction, and [PERSON] et al.'s method based on deep convolutional neural network are used as comparison algorithms to reconstruct the data set with Gaussian noise. Three sets of images are randomly selected from the test set. The reconstruction results of different reconstruction algorithms under the same noise (\(3\times 10^{-4}\)) are shown in Figure 17.  
At the same time, PSNR and SSIM are used to compare the advantages and disadvantages of each reconstruction algorithm. The reconstructed results in Figure 17 are used as the evaluation index images of each method to obtain the evaluation index values. The results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Classes**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{**Phase**} \\ \cline{3-6}  & & **PSNR (dB)** & **SSIM** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{6}{*}{\(1\)} & G-S & 25.538 & 0.739 & 7.090 & 0.186 \\  & A-S & 25.616 & 0.742 & 7.117 & 0.189 \\ \cline{1-1}  & [PERSON] & 23.807 & 0.935 & 21.255 & 0.900 \\ \cline{1-1}  & [PERSON] & 25.126 & 0.812 & 19.467 & 0.886 \\ \cline{1-1}  & STPM-FPM & 36.000 & 0.970 & 23.414 & 0.963 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm comparison results of multiple sets of images under \(3\times 10^{-4}\) noise.  
Figure 16: Truth image.  
than the phase reconstruction results of G-S and A-S methods. Compared with the methods of [PERSON] et al. and [PERSON] et al., the experimental results of this paper have no obvious artifacts, and contain more texture details and have reduced error. In Table 2, the result of the red part is the best, and the blue part is the second best. For the reconstruction index value, the proposed method is better than the other four methods, while [PERSON]'s method has sub-optimal performance.