# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 5 Conclusions  
In this work, we propose an enhanced multi-network prior method for single-pixel imaging reconstruction, which utilizes the advantages of SAE and Unet networks to extract different prior information. The SAE network is for primary reconstruction, and the Unet network is for deep reconstruction. In order to make full use of the measurement value dimension information, the SAE network is designed as four layers. In order to enhance the generalization of the SAE network, the decoding matrix is obtained by solving the group inverse of the encoding matrix. The Unet network uses three different sizes of convolution kernels, residual networks and attention mechanisms to improve feature extraction capabilities. Finally, the output of the two networks is fused, and the fusion training process automatically adjusts the weight. Through simulation analysis and experimental verification, the reconstruction effect of our proposed method is better than that of TVAL3. The proposed method is suitable for a variety of scenarios and has development prospects.  
Figure 11: This is the reconstruction results of sampled images in the SPI system. (**a**,**c**) are the reconstruction results of TVAL3; (**b**,**d**) are the reconstruction results of EMNP-SPI.  
This method can reconstruct the image at a 0.05 measurement rate, but the reconstruction quality is worse than that of DGRN, MAID-GAN, and other generation models at lower measurement rates. The reconstruction result of DGRN, MAID-GAN and EMNP-SPI is shown in Figure 12. It can be seen from the figure that DGRN and MAID-GAN reconstruction image clarity and PSNR are better than our proposed method.  
The reason for this result is that the generated model has strong feature extraction ability. The generated model can reconstruct a clear image based on a few measured values. But the image reconstructed by the generated model is different from the original image, which involves a trade-off between clarity and authenticity. In very low measurement rate reconstruction, the generated model selects clarity prior to authenticity, and our method selects authenticity prior to clarity. Our method needs to be further optimized to enhance the feature extraction ability. This method improves the performance of traditional methods at low measurement rates. In the future, we will continue to study in this direction.  
Conceptualization, J.F. and Q.L.; funding acquisition, H.W.; methodology, J.F. and J.D.; investigation, J.F. and Q.Z.; software, J.D.; visualization, J.F. and Q.Z.; writing\(-\)original draft preparation, J.F.; writing\(-\)review and editing, J.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Basic Research Plan in Shaarol Province of China, grant number 2023-JQ-QC-0714.  
Not applicable.  
The data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
[PERSON] has made contributions to the visualization of natural datasets and the splicing of small images into large ones.  
The authors declare no conflicts of interest.  
Figure 12: This is the reconstruction results of different methods.