## 4 Experiments and Comparisons
### Experimental Analysis  
We analyze the above algorithm from the perspective of sensory effects. As shown in Figures 10-13, we again compare our proposed method with the six existing methods. Based on the obtained results, our method is capable to clearly reconstruct the textures, lines and edges of the infrared image, thus improving the image quality. The experimental results of enlarging the \"dock\" picture three times in the test1 are shown in Figure 10. The image recovered by our method shows a better visual experience, and the background clarity is also improved. More specifically, the gap between the pavilions and railings on the wharf is clearly visible. The glossiness and smoothness of the columns in the figure are \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Dataset** & **Scale** & **Bicubic** & **SRCNN [4]** & **FSRCNN [5]** & **VDSR [6]** & **DRCN [7]** & **LapSRN [8]** & **RCAN [9]** & **Ours** \\ \hline Test1 & 2\(\times\) & 0.8853 & 0.9087 & 0.9134 & 0.9302 & 0.9284 & 0.9299 & 0.9315 & **0.9349** \\ Test2 & 2\(\times\) & 0.8866 & 0.9100 & 0.9147 & 0.9315 & 0.9297 & 0.9312 & 0.9328 & **0.9362** \\ Test3 & 2\(\times\) & 0.8949 & 0.9183 & 0.9230 & 0.9398 & 0.9380 & 0.9395 & 0.9411 & **0.9445** \\ Test4 & 2\(\times\) & 0.8992 & 0.9226 & 0.9273 & 0.9441 & 0.9423 & 0.9438 & 0.9454 & **0.9488** \\ Test1 & 3\(\times\) & 0.8579 & 0.8813 & 0.8860 & 0.9028 & 0.9010 & 0.9025 & 0.9041 & **0.9075** \\ Test2 & 3\(\times\) & 0.8623 & 0.8856 & 0.8903 & 0.9071 & 0.9053 & 0.9068 & 0.9084 & **0.9118** \\ Test3 & 3\(\times\) & 0.8635 & 0.8871 & 0.8917 & 0.9085 & 0.9067 & 0.9082 & 0.9098 & **0.9132** \\ Test4 & 3\(\times\) & 0.8800 & 0.9032 & 0.9081 & 0.9249 & 0.9231 & 0.9246 & 0.9262 & **0.9296** \\ Test1 & 4\(\times\) & 0.7747 & 0.7982 & 0.8028 & 0.8196 & 0.8178 & 0.8193 & 0.8209 & **0.8243** \\ Test2 & 4\(\times\) & 0.7926 & 0.8159 & 0.8206 & 0.8374 & 0.8356 & 0.8371 & 0.8387 & **0.8421** \\ Test3 & 4\(\times\) & 0.8001 & 0.8236 & 0.8282 & 0.8450 & 0.8432 & 0.8447 & 0.8463 & **0.8497** \\ Test4 & 4\(\times\) & 0.8056 & 0.8289 & 0.8337 & 0.8505 & 0.8487 & 0.8502 & 0.8518 & **0.8552** \\ \hline \end{tabular}
\end{table} 
Table 4: Average SSIM for different algorithms at Test1, Test2, Test3 and Test4 datasets. Figure 9: Compared results of the PSNR and SSIM on Test1 and Test4 datasets. also significantly improved, and the clouds and buildings in the distance are also nicely recovered. In contrast, other algorithms have more or less deficiencies and unsatisfactory aspects in reconstruction. Figure 11: Comparison of restored results on platform by different super-resolution methods (4\(\times\)). (**A**) HR (**B**) Bicubic (**C**) SRCNN (**D**) FSRCNN (**E**) VDSR (**F**) DRCN (**G**) LapSRN (**H**) RCAN (**I**) Ours. Figure 10: Comparison of restored results on deck by different super-resolution methods (3\(\times\)). (**A**) HR (**B**) Bicubic (**C**) SRCNN (**D**) FSRCNN (**E**) VDSR (**F**) DRCN (**G**) LapSRN (**H**) RCAN (**I**) Ours. Figure 11 shows the experimental results obtained from the \"platform\" picture using test2 data that are magnified by 4\(\times\). The image restored by our method is closer to the real image. The lines and edges of the fence on the platform are clearly visible. The restoration effect of the SRCNN algorithm is obviously blurred, and the images restored by the LapSRN and the RCAN algorithms are too rigid in terms of contours and lines. Figure 12 shows the results of the experiment obtained from the \"bridge\" picture using the test3 data that are magnified by 2\(\times\). The DRCN and the VDSR algorithms unable to restore the bridge frame line, or even to see the details and contours clearly, while the SRCNN, the FSRCNN and other algorithms distort the edges of marks. Our method can restore clear frame lines and visible edges of the bridge, as the silhouette behind the bridge looks more intuitive. Figure 12: Comparison of restored results on bridge by different super-resolution methods (4\(\times\)).