## 3 Results
### Super-Resolution Performance Test  
The experimental setup consisted of a PC host with an i7-4720 HQ CPU (Intel Corporation, Santa Clara, CA, USA) and GTX960 GPU (Nvidia Corporation, Santa Clara, CA, USA). The specific parameter settings for both the reconstruction and training parts of the dictionary set are provided and explained in Section 2. It is important to note that dictionary set learning takes place after the image segmentation process. For training purposes, we randomly selected 100,000 windows from 54 training scenes, each with dimensions of \(5\times 5\). No noise reduction algorithm was employed following the non-uniformity correction and blind element repair of the captured images. Consequently, the resulting images may have contained electronic noise, dark current noise [34], stray light response, and some uncorrected blind elements. The objective evaluation indicators for the simulation results are depicted in Table 1.  
The table above clearly demonstrates the excellent reconstruction performance achieved by the proposed method. All five algorithms yielded good results for the \(S1\) and \(S2\) images. The majority of pixels in the \(S1\) and \(S2\) images represented the low-frequency component. Despite this, our approach still stood out and achieved the best results. Unlike the other algorithms, our method did not obtain the \(S1\) and \(S2\) images by detecting the difference in declination angle. On the \(S0\) index, the MPPOCS method exhibited a low reconstruction effect. This is because the filtering method used in this algorithm sacrifices some details of the decline-detecting image, reduces the noise of the orthogonal difference image, and neglects the quality of the weighted image. In our reproductions of the other algorithms, some did \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **S0** & **S1** & **S2** & **DP** & **AP** \\ \hline \multirow{3}{*}{EARI} & SSIM = 0.9649 & SSIM = 0.9670 & SSIM = 0.9751 & SSIM = 0.8148 & SSIM = 0.7559 \\  & PSNR = 35.6509 & PSNR = 41.4362 & PSNR = 42.4668 & PSNR = 27.9892 & PSNR = 19.8982 \\  & RMSE = 0.0165 & RMSE = 0.0085 & RMSE = 0.0075 & RMSE = 0.0399 & RMSE = 0.1012 \\ \hline \multirow{3}{*}{NP} & SSIM = 0.9708 & SSIM = 0.9807 & SSIM = 0.9842 & SSIM = 0.8599 & SSIM = 0.7749 \\  & PSNR = 36.8702 & PSNR = 43.5596 & PSNR = 44.0752 & PSNR = 29.7688 & PSNR = 23.2195 \\  & RMSE = 0.0143 & RMSE = 0.0066 & RMSE = 0.0063 & RMSE = 0.0324 & RMSE = 0.0690 \\ \hline \multirow{3}{*}{PCDP} & SSIM = 0.9723 & SSIM = 0.9748 & SSIM = 0.9827 & SSIM = 0.8512 & SSIM = 0.7602 \\  & PSNR = 37.1946 & PSNR = 42.4574 & PSNR = 43.67161 & PSNR = 29.2385 & PSNR = 22.4482 \\  & RMSE = 0.0138 & RMSE = 0.0075 & RMSE = 0.0066 & RMSE = 0.0345 & RMSE = 0.0754 \\ \hline \multirow{3}{*}{MPPOCS} & SSIM = 0.9687 & SSIM = 0.9972 & SSIM = 0.9952 & SSIM = 0.9610 & SSIM = 0.9452 \\  & PSNR = 36.1099 & PSNR = 54.8412 & PSNR = 50.9966 & PSNR = 38.9372 & PSNR = 38.6978 \\ \cline{1-1}  & RMSE = 0.0156 & RMSE = 0.0018 & RMSE = 0.0028 & RMSE = 0.0113 & RMSE = 0.0116 \\ \hline \multirow{3}{*}{This Work} & SSIM = 0.9809 & SSIM = 0.9980 & SSIM = 0.9972 & SSIM = 0.9729 & SSIM = 0.9595 \\ \cline{1-1}  & PSNR = 40.0757 & PSNR = 56.8644 & PSNR = 54.0460 & PSNR = 41.8172 & PSNR = 41.3630 \\ \cline{1-1}  & RMSE = 0.0099 & RMSE = 0.0014 & RMSE = 0.0019 & RMSE = 0.0081 & RMSE = 0.0086 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The objective evaluation indexes of each algorithm for a tower. not align with the original data. We analyzed these replications in our experiments using the publicly available source code provided by the original authors. The primary difference lay in the down-sampling technique. The authors used interlaced pixel extraction for the simulation, resulting in simulated images with minimal noise. In contrast, our simulated image contained significantly more noise. Additionally, our degradation process employs bicubic interpolation to obtain the subsampled image, where the pixels in the neighborhood are double cubic-weighted.