# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 4 Experimental Results and Analysis
### Comparison and Analysis of Reconstruction Methods under Different Noise
#### 4.3.4 Real Image Analysis  
For verifying the advantages and disadvantages of the reconstructed results of the STPM-FPM method in this paper under the data collected on real devices, this section uses a fine-tuned data set with small samples to test the reconstruction results, and the reconstruction results are compared and analyzed. Four real collected intensity images of blood cell smear, penis tissue section, alveolar tissue section and testis tissue section were selected as reconstructed contrast images. The traditional reconstruction methods G-S, A-S, [PERSON] et al.'s method based on neural networks, and [PERSON] et al.'s method based on deep convolutional neural network are selected for comparison and analysis. In the process of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reconstruction Method** & **Iteration Times** & **Reconstruction Time** \\ \hline G-S & 50 & 2.170 s \\ \hline A-S & 50 & 2.744 s \\ \hline [PERSON] et al. & 20 & 33.503 s \\ \hline [PERSON] et al. & 20 & 204.304 s \\ \hline STPM-FPM & 0 & 0.139 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time comparison of different reconstruction algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Size**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{**Phase**} \\ \cline{3-6}  & & **PSNR (dB)** & **SSIM** & **PSNR (dB)** & **SSIM** \\ \hline \multirow{4}{*}{\(1\times 10^{-4}\)} & G-S & 27.978 & 0.794 & 12.052 & 0.228 \\  & A-S & 27.970 & 0.790 & 12.046 & 0.227 \\  & Zhang & 21.067 & 0.845 & 23.605 & 0.936 \\  & Jiang & 18.209 & 0.767 & 19.909 & 0.854 \\  & STPM-FPM & 33.861 & 0.971 & 26.093 & 0.951 \\ \hline \multirow{4}{*}{\(2\times 10^{-4}\)} & G-S & 24.492 & 0.650 & 12.295 & 0.246 \\  & A-S & 24.468 & 0.647 & 12.280 & 0.246 \\  & Zhang & 21.083 & 0.849 & 23.534 & 0.935 \\  & Jiang & 18.209 & 0.767 & 19.909 & 0.854 \\  & STPM-FPM & 34.649 & 0.965 & 26.787 & 0.947 \\ \hline \multirow{4}{*}{\(3\times 10^{-4}\)} & G-S & 22.693 & 0.562 & 12.497 & 0.262 \\  & A-S & 22.683 & 0.562 & 12.490 & 0.261 \\ \cline{1-1}  & [PERSON] & 20.334 & 0.838 & 24.100 & 0.933 \\ \cline{1-1}  & [PERSON] & 18.209 & 0.767 & 19.909 & 0.854 \\ \cline{1-1}  & STPM-FPM & 36.949 & 0.982 & 25.889 & 0.943 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconstruction index of the same group of images under different noise conditions.  
using Fourier ptychographic microscopy to collect real data, only the low-resolution image of the original image sample can be collected, and there is no true value of the intensity image and the phase image. Therefore, this experiment can only be qualitatively evaluated by comparing with the reconstruction results of the four methods.  
As shown in Figure 19, according to the results, under the premise of adding some real collected data, the experimental method in this paper still has better reconstruction performance and visual effect. Compared with the traditional algorithm, the STPM-FPM algorithm has a more obvious effect of removing artifacts. At the same time, it has better reconstruction result clarity from the naked eye. The background error of the reconstructed image is relatively less, and there are clearer texture details, which proves the generalization and robustness of STPM-FPM in the real acquisition data set. Thanks to the end-to-end processing based on the deep learning method and the acceleration of the image processor, the operation rate of the proposed method is higher than that of the other three comparison algorithms, which is helpful for the practical application of Fourier ptychographic imaging under high temporal resolution.  
Figure 19: _Cont._