## 3 Experiment and Discussion
### Dual-Style Experiment
In the 2\(\times\) and 4\(\times\) super-resolution tasks, the trend is consistent, with the RMSE value decreasing as the super-resolution factor increases, indicating that the quality of the generated images improves as the resolution increases. However, in the 8x super-resolution task, although the RMSE value continues to decrease in most cases, there is a brief rebound in the RMSE value for the generated image at 1024 \(\times\) 1024. This rebound phenomenon warrants attention, although the overall trend remains one of improvement. And the other visual metrics (PSNR, SMMI, LPILS) show similar performance. This phenomenon suggests that, despite the potential difficulty in accurately reconstructing certain complex details or structures at extremely high resolutions (such as the 1024 \(\times\) 1024 image in the 8\(\times\) task), due to the advantages offered by StyleGAN-v2, Dspix2 pix is still able to effectively generate high-quality super-resolution images in most cases, thereby improving image quality and reducing visual artifacts. However, for the ERGAS and SAM metrics, the values do not show improvement with the increase in image size. This indicates that the results produced by DSpix2 pix are not suitable for tasks requiring precise remote sensing parameter inversion. To prove that the additional style structure is necessary, we selected the 2\(\times\) super-resolution task at the 1024 quality. We compared the super-resolution results with and without the additional style structure. The comparison results are shown in Table 8.  
In Table 8, it is evident that the additional style injection significantly improves the performance of the super-resolution network. The improvement in RMSE exceeds 40. The \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RMSE \(\downarrow\)** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **ERGAS \(\downarrow\)** & **LPIPS \(\downarrow\)** & **SAM \(\downarrow\)** \\ \hline with extra & \multirow{2}{*}{**35.87**} & \multirow{2}{*}{**26.56**} & \multirow{2}{*}{**0.84**} & \multirow{2}{*}{**3.21**} & \multirow{2}{*}{**0.18**} & \multirow{2}{*}{**15.08**} \\ style & & & & & & \\ without & & & & & & \\ extra & 75.19 & 21.03 & 0.77 & 3.41 & 0.30 & 15.52 \\ style & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results at 1024 \(\times\) 1024 quality in 2\(\times\) super-resolution tasks with and without additional style injection. Figure 8: Schematic diagram of discount comparison of super resolution results under different tasks and scales of the same network. (**a**) is RMSE; (**b**) is PSNR; (**c**) is SSMI; (**d**) is ERGAS; (**e**) is LPIPS; (**f**) is SAM. key lies in that, as the quality increases, the L1 loss provided by pix2 pix contributes more significantly, causing the entire image to exhibit a uniform style dominated by the main land cover. The additional style information we inject helps to restore the image to a more realistic ground style. The following example from the dataset can illustrate this point, as shown in Figure 9.  
As shown in Figure 9, at the 1024 \(\times\) 1024 scale, the entire image without additional style injection displays a green hue dominated by farmland, with urban areas and roads exhibiting the same coloration. However, when additional style from an example image is injected, this issue is significantly alleviated. Therefore, the additional style injection is also necessary in DSpix2 pix.