## 4 Experiments and Comparisons
### Experimental Analysis  
In this section, we analyze our designed algorithm from the perspective of subjective vision. As shown in Figures 13-16, we again conducted the sensory comparison with the six state-of-the-art algorithms mentioned previously in this paper. Our method was capable of effectively restoring the lines, edges, and details of the image, thus improving the resolution ratio of the remote sensing images. The experimental values of the 3\(\times\) \"P0283\" airport picture in Dataset 1 is shown in Figure 13. The result achieved using our algorithm showed a better visual experience on the reconstructed aircraft wings with a clearly defined background. The edges and lines of the wing were relatively uniform and smooth. Similarly, the shadows on the ground were also clearly visible. It is not difficult to see that, when our algorithm was applied to the reconstruction of the image, particularly when restoring edges on objects and differentiating them from their backgrounds, the lines between the grass and the road in the middle of the image, as well as the white lines on the right side of the image, all appeared straight, clear, and hierarchical, while the results obtained using the other algorithms still had various degrees of blur or distortion. Figure 14 shows the experimental values calculated from the 3\(\times\) \"N0506\" urban picture using Dataset 2. The image recovered using our method was closer to the ground-truth image. The outline of the small light rail train was clearly visible, and the oil tanker beside it was also relatively easy to identify. The lines and corners of tall buildings in the figure were displayed intuitively. On the other side, the reconstruction effect of SRCNN was vague, and objects could hardly be recognized. The LapSRN and SRGAN algorithms also produced results with limited clarity, particularly in the contours and lines of small trains. Figure 15 shows the values of the experiment on the 3\(\times\) \"N0502\" house picture using Dataset 3. The images restored by VDSR and EDSR showed a little distortion along the line of the roof. SRCNN, FSRCNN, and other algorithms also had shortcomings when restoring car windows, edges, and bodies. In contrast, lines between the side windows of the car on the far right could be clearly identified from the image obtained using our method, and the outline of the roof was more intuitive. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Dataset** & **Scale** & **Bicubic** & **SRCNN** & **FSRCNN** & **VDSR** & **EDSR** & **LapSRN** & **SRGAN** & **Ours** \\ \hline
**Dataset1** & 3\(\times\) & 1.88 & 2.45 & 2.63 & 3.12 & 3.19 & 3.28 & 3.34 & **3.41** \\
**Dataset2** & 3\(\times\) & 1.76 & 2.31 & 2.55 & 3.06 & 3.15 & 3.17 & 3.26 & **3.35** \\
**Dataset3** & 3\(\times\) & 1.53 & 2.24 & 2.48 & 2.97 & 3.01 & 3.06 & 3.11 & **3.17** \\
**Dataset4** & 3\(\times\) & 1.63 & 2.28 & 2.37 & 2.99 & 3.07 & 3.08 & 3.19 & **3.23** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average MOS values with different algorithms on Dataset 1, Dataset 2, Dataset 3, and Dataset 4. Figure 12: Compared MOS results with a 3\(\times\) scale on Dataset 1, Dataset 2, Dataset 3, and Dataset 4. Figure 16 shows the values of the experiment calculated from the 3\(\times\) \"P0808\" airport picture using Dataset 4. The image reconstructed using the FSRCNN and EDSR algorithms led to a distortion of the aircraft with added deformation and artefacts, and these algorithms could not completely restore the shadow part of the aircraft tail to their normal shapes. The reconstructed image obtained using our method, on the other hand, could clearly show not only the white lines on the ground between the two aircrafts, but also the tail shadow of the aircraft. Therefore, our proposed algorithm in this paper proves its clear advantage in restoring lines, edges, and details to a degree closer to the real image. Below, we introduce the limitations of our research.