# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 4 Result and Discussion
### Simulation Experiment Results on Natural Image Dataset  
Ninety-one images of set91 are divided into 64 \(\times\) 64 patches with a stride of 16. Ten thousand of them are used as the training dataset and 500 are used as the test dataset. After training based on the natural image dataset, set5 is used for testing. The five images of the set5 dataset are divided into 64 \(\times\) 64 block images according to the step size of 64. If the image length or width can be divided by 64, there is no overlapping area in the image. If the image length or width cannot be divided by 64, there will be overlapping areas in the image. The test image is reconstructed and spliced into an image of the original image size.  
In this section, we verify the reconstruction performance of EMNP-SPI on the gray natural image dataset. Table 4 shows the PSNR and SSIM of EMNP-SPI on the set5 dataset. The reconstruction images at MR = 5% are shown in Figure 7, and the comparative simulation results of EMNP-SPI and other reconstruction methods are shown in Figure 8. It can be seen from Figure 7 that a good quality image was reconstructed at a 5% measurement rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Method**} & **MR = 3\%** & **MR = 5\%** & **MR = 8\%** & **MR = 10\%** \\ \cline{3-6}  & & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline baby & EMNP-SPI & 16.23/0.42 & 20.47/0.56 & 22.34/0.76 & 24.68/0.84 \\ bird & EMNP-SPI & 15.96/0.39 & 19.48/0.52 & 21.74/0.72 & 23.88/0.82 \\ butterfly & EMNP-SPI & 16.14/0.41 & 20.67/0.57 & 22.67/0.78 & 25.24/0.85 \\ head & EMNP-SPI & 15.93/0.40 & 20.27/0.54 & 22.04/0.74 & 24.82/0.84 \\ lenna & EMNP-SPI & 16.43/0.43 & 20.87/0.58 & 22.64/0.79 & 24.91/0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PSNR and SSIM of EMNP-SPI at different MR.  
Figure 6: This figure is the reconstruction result of different methods of letter “UCAS” under different measurement rates. The four networks are trained using the flower dataset, and the \(64\times 64\) grayscale image letter “UCAS” is used as the test image to reconstruct the \(64\times 64\) grayscale image.  
Figure 8: This is the reconstruction results of “baby” using different algorithms. (**a**) TVAL3; (**b**) SAE (Reference [41]); (**c**) SPI-GAN; (**d**) EMNP-SPI.  
Figure 7: This figure is the reconstruction result of the EMNP method at MR = 5%. Using the set91 dataset as the training dataset, the image is divided into 64 \(\times\) 64 grayscale images for training, and the set5 grayscale image divided into 64 \(\times\) 64 is used as the test image. After reconstruction, the 64 \(\times\) 64 grayscale image is obtained, and the small image is spliced into a large image. (**a**) Original images in set5; (**b**) reconstruction results of EMNP-SPI.  
It can be seen from Figure 8 that EMNP-SPI can reconstruct clear images at MR = 5%, and the reconstruction quality is better than that of other algorithms. TVAL3 can reconstruct a clear image at MR = 8%, the reconstructed image of SAE has partially missing details at MR = 10%, and SPI-GAN has obvious blocks in the reconstructed image with low measurement rate.  
We compare the proposed algorithm with TVAL3, SAE, and SPI-GAN. It can be seen from Figure 9 that the EMNP-SPI method is superior to TVAL3 at low MR in PSNR and SSIM. At MR = 5%, PSNR is 4.98 dB higher, and SSIM is 0.11 higher than TVAL3.