## 5 Results and Analysis
### Quantitative Evaluation  
Table 2 presents the PSNR and SSIM results across different models, providing a quantitative assessment of their reconstruction performance. Additionally, Table 3 showcases a comprehensive comparison of model parameters and inferencing speed, offering a holistic view of the models' overall capabilities, considering both reconstruction performance and efficiency. PSNR and SSIM evaluation metrics represent the average values across all images. Figure 6: Comparison of the results of super-resolution reconstruction generated by different methods with real remote sensing images. Table 2 indicates that despite being a lightweight network, the SRRepViT model outperforms the ECBSR model in PSNR and SSIM by 0.3 and 0.015, respectively. Notably, the SRRepViT model exhibits slightly superior reconstruction performance compared to full-size models like EDSR, SwinIR, and RCAN, showing a PSNR lead of 0.02-0.005 and an SSIM lead of 0.01 on the same dataset. In Table 3, SRRepViT demonstrates significant reductions in FLOPs and parameters compared to ECBSR, with inferencing time notably shorter than all full-size models. The FLOPs, parameters, and inferencing time of SRRepViT are 48%, 21%, and 52% of ECBSR's, respectively, while maintaining better reconstruction performance. Moreover, when compared to other models with similar reconstruction accuracy, SRRepViT remarkably reduces FLOPs and parameters. Despite a slightly lower super-resolution accuracy than the HAT model, SRRepViT's FLOPs and parameters stand at a mere 0.7% and 0.6% of HAT's, aligning well with lightweight model and mobile deployment requirements. In Table 4, the SRRepViT ablation experiment reveals the ViT module's substantial impact on enhancing super-resolution reconstruction, increasing PSNR by 0.2, SSIM by 0.01, and concurrently reducing FLOPs and parameters. Incorporating the subsequent channel attention module and further refining pixel shuffle improves reconstruction accuracy but comes with increased FLOPs, parameters, and inference time. However, the use of structural reparameterization significantly slashes FLOPs and parameters by 50%, reducing the time for inferring a single image by 62% while maintaining reconstruction accuracy. In Figure 7, a comprehensive comparison of network performance in terms of model inference speed, complexity, and super-resolution reconstruction capability is presented. The vertical axis represents super-resolution reconstruction performance (PSNR), where SRRepViT ranks at the forefront among the existing networks, slightly trailing behind HAT. Compared to the HAT model, our proposed SRRepViT model has only 0.7% of the FLOPs and 0.6% of the params of the HAT model, with the inference time per single image being only 0.9% of the HAT model. The horizontal axis denotes model efficiency, displaying SRRepViT's lead over all other networks in the experiment regarding inferencing speed. Additionally, the size of the model shapes represents the memory footprint, showcasing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **PSNR** & **SSIM** & **FLOPs** & **Params** & **Speed** \\ \hline ECBSR & 30.6891 & 0.8291 & 36.32 G & 0.78 M & 0.025 s \\ +ViT & 30.8917 & 0.8382 & 17.56 G & 0.29 M & 0.015 s \\ + ViT + attention & 30.9176 & 0.8401 & 30.02 G & 0.49 M & 0.020 s \\ + ViT + attention & 30.9528 & 0.8429 & 31.44 G & 0.51 M & 0.021 s \\ + pixel shuffle + re-parameterization & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Ablation study of SRRepViT. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **ECBSR** & **EDSR** & **SwinSR** & **RCAN** & **SRRepViT** & **HAT** \\ \hline PSNR & 30.6891 & 30.9186 & 30.9453 & 30.9379 & 30.9528 & 31.1809 \\ SSIM & 0.8291 & 0.8361 & 0.8377 & 0.8366 & 0.8429 & 0.8434 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PSNR and SSIM results of different methods.