## 4 Experiments and Comparisons
### Experimental Comparison  
We divided the remain 200 graphs datasets into four test datasets, containing 20, 30, 50, and 100 pairs of images, and we labeled them as Dataset 1, Dataset 2, Dataset 3, and Dataset 4, respectively. The trained model in this paper was sequentially tested using the images of each dataset, and our testing results were evaluated using the following objective reconstruction and subjective visual evaluation indicators: the peak signal-to-noise ratio (PSNR) [30], the structural similarity (SSIM) [31], and the mean opinion score (MOS) [9]. After finishing running the tests, we obtained a series of results. The average PSNR indicators of Dataset 1, Dataset 2, Dataset 3, and Dataset 4 were 31.15 dB, 31.72 dB, 32.06 dB, and 32.64 dB, and the average SSIM indicators were 0.8034, 0.8142, 0.8179, and 0.8255, respectively. In order to prove that our model algorithm was superior compared to other algorithms, we also conducted simulation experiments using SRCNN [4], FSRCNN [5], VDSR [6], EDSR [7], LapSRN [8], and SRGAN [9]. As shown in Table 1, the structures of the related networks are displayed in terms of the training time and the number of parameters of each model. Figure 10: The training curves of the network. The objective evaluation index result for each network model was recorded as shown in Table 2. Impressively, the PSNR value generated by our algorithm was superior to that of other advanced algorithms. On Dataset 2 with three amplifications, the average PSNR index of our algorithm was 0.46 dB higher than SRGAN's result and 0.96 dB higher than EDSR's result. Similarly, it is also clear on Dataset 4 that the average PSNR index generated by our algorithm was 0.82 dB higher than VDSR's value and 2.73 dB higher than SRCNN's value. In Table 3, the average SSIM result calculated using our algorithm on Dataset 3 with three amplifications was 0.005 higher than EDSR's result and 0.0332 higher than FSRCNN's result. The average SSIM result calculated using our algorithm was 0.009 higher than VDSR's value and 0.0396 higher than SRCNN's value on Dataset 4. Figure 11 shows the values of PSNR and SSIM on Dataset1 and Dataset4 with different methods, which directly indicates that our model has advantages over others. For the subjective intention score MOS, we invited 50 students from the laboratory to conduct a subjective survey by giving their scores, and then calculated the average values. The score reference was based on the following grades: excellent (5 points), good (4 points), general (3 points), poor (2 points), and extremely poor (1 point). As shown in Table 4 and Figure 12, we can clearly identify that our algorithm not only had a significant recovery \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Scale** & **Bicubic** & **SRCNN** & **FSRCNN** & **VDSR** & **EDSR** & **LapSRN** & **SRGAN** & **Ours** \\ \hline
**Dataset 1** & 3\(\times\) & 26.54 & 28.43 & 28.56 & 30.37 & 30.23 & 30.34 & 30.70 & **31.15** \\
**Dataset 2** & 3\(\times\) & 26.59 & 28.98 & 29.10 & 30.91 & 30.76 & 30.87 & 31.26 & **31.72** \\
**Dataset 3** & 3\(\times\) & 27.36 & 29.52 & 29.66 & 31.47 & 31.32 & 31.43 & 31.61 & **32.06** \\
**Dataset 4** & 3\(\times\) & 27.83 & 29.91 & 30.04 & 31.82 & 31.65 & 31.77 & 32.18 & **32.64** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average PSNR on Dataset 1, Dataset 2, Dataset 3, and Dataset 4 with different algorithms. Figure 11: Compared PSNR and SSIM results with a 3\(\times\) scale on Dataset 1 and Dataset 4.