## 3 Experiment and Discussion
### Dual-Style Experiment  
To demonstrate that the dual-style addition is beneficial to the network, we designed the following experiment. First, since StyleGAN-v2 incorporates the injected style within the convolutional layers, making it difficult to remove, this paper extracts super-resolution images generated at different qualities (128 \(\times\) 128, 256 \(\times\) 256, 512 \(\times\) 512, 1024 \(\times\) 1024) and calculates the same experimental metrics as in Experiment 3.1. The experimental results are shown in Tables 5-7. From the data in the table, it is evident that the super-resolution results for the 1024 \(\times\) 1024 large-scale images are generally better compared to the results at lower scales. To more intuitively observe the data, we plotted a line graph, as shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multicolumn{4}{c}{**Downsamples 4\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 70.04 & 21.01 & 0.51 & 3.16 & 0.77 & **14.75** \\
128 \(\times\) 128 & 75.70 & 20.77 & 0.64 & 3.17 & 0.45 & 15.15 \\
256 \(\times\) 256 & 61.74 & 22.38 & 0.65 & **3.14** & 0.45 & 15.06 \\
512 \(\times\) 512 & 59.72 & 22.60 & 0.64 & 3.15 & 0.51 & 14.96 \\
1024 \(\times\) & **48.35** & **24.23** & **0.72** & 3.35 & **0.34** & 15.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of super-resolution results of Dépix2 pix at different scales in 4\(\times\) super-resolution tasks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multicolumn{4}{c}{**Downsamples 8\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 83.85 & 19.40 & 0.48 & 3.27 & 0.75 & **14.87** \\
128 \(\times\) 128 & 71.48 & 20.88 & 0.55 & 3.77 & 0.64 & 14.97 \\
256 \(\times\) 256 & 70.74 & 21.01 & 0.56 & 3.21 & 0.61 & 14.96 \\
512 \(\times\) 512 & **57.35** & **22.91** & **0.63** & 3.33 & **0.50** & 14.90 \\
1024 \(\times\) & 68.19 & 21.96 & 0.61 & **3.20** & 0.54 & 14.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of super-resolution results of Dépix2 pix at different scales in 8\(\times\) super-resolution tasks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Region**} & **Bicubic** & **pix2 pix** & **CycleGAN** & **DRIT** & **UNIT** & **SRGAN** & **DSpix2 pix** \\ \hline Global & 36.81 & 37.71 & **27.12** & 34.76 & 36.04 & 36.27 & **32.33** \\ \# 1 & 23.07 & 30.69 & **19.12** & 21.6 & 30.69 & 23.3 & **20.54** \\ \# 2 & 38.74 & 46.98 & **24.94** & 35.56 & 40.74 & 39.07 & **34.79** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Gray-level standard deviation for different models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scale**} & \multicolumn{4}{c}{**Downsamples 2\(\times\)**} \\ \cline{2-7}  & **RMSE**\(\downarrow\) & **PSNR**\(\uparrow\) & **SSMI**\(\uparrow\) & **ERGAS**\(\downarrow\) & **LPIPS**\(\downarrow\) & **SAM**\(\downarrow\) \\ \hline Bicubic & 53.05 & 23.49 & 0.58 & **2.97** & 0.65 & **14.47** \\
128 \(\times\) 128 & 65.87 & 21.90 & 0.78 & 3.09 & 0.27 & 15.38 \\
256 \(\times\) 256 & 61.70 & 22.07 & 0.79 & 3.16 & 0.32 & 15.25 \\
512 \(\times\) 512 & 57.57 & 22.96 & 0.82 & 3.17 & 0.21 & 15.22 \\
1024 \(\times\) & **35.87** & **26.56** & **0.84** & 3.11 & **0.18** & 15.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of super-resolution results of Dépix2 pix at different scales in 2\(\times\) super-resolution tasks. In Figure 8, it can be observed that as the generated image quality increases, visual indicators (such as RMSE) show a gradual improvement.