## 6 Ablation Studies for LinearTGAN  
In ablation studies, we systematically evaluated the architectural design and key components of the LinearTGAN model to assess their contributions to image reconstruction quality and overall performance. We trained the model and evaluated its PSNR and LPIPS (VGG) performance using a self-constructed acoustic dataset of 200 randomly selected \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**System** & \begin{tabular}{c} **Step Size** \\ **(\(\upmu\))** \\ \end{tabular} & **SR Applied** & \begin{tabular}{c} **Original** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Enhanced** \\ **Resolution** \\ \end{tabular} & \begin{tabular}{c} **Scanning** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Processing** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Scanning** \\ **Performance** \\ \end{tabular} \\ \hline TSAM-400 & 50 & No & 6000 \(\times\) 6000 & - & 2727 & 0 & 2727 & 0\% \\ USI RSAM 300 & 50 & No & 6000 \(\times\) 6000 & - & 763 & 0 & 763 & 72\% \\ USI RSAM 300 & 100 & Yes & 3000 \(\times\) 3000 & 6000 \(\times\) 6000 & 190 & 16 & 206 & 92\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of scanning performance. Figure 7: Deployment framework of the proposed model. images, each with a 256 \(\times\) 256 pixel resolution, ensuring no overlap with training and test datasets. This analysis provides a comprehensive understanding of the effectiveness of each component in improving SR capabilities. **Effect of Swin Transformer Blocks (STBs)**: Figure 8 presents an in-depth analysis of the impact of STBs on the performance of LinearTGAN. The results indicate that increasing the number of STBs from 4 to 8 significantly enhances image reconstruction quality, improving the PSNR from 24.917 dB to 26.864 dB and reducing the LPIPS from 0.674 to 0.465. These improvements highlight the essential role of STBs in capturing long-range dependencies, refining fine textures, and enhancing structural coherence. However, beyond 8 STBs, performance improvements diminish, with the PSNR stabilizing at 26.895 dB for 12 blocks, while LPIPS fluctuates slightly to 0.417. Thus, the -eight-STB configuration achieves an optimal balance between image quality and efficiency, making it the most suitable choice for high-precision SR tasks within the LinearTGAN framework. **Effect of Residual Swin Transformer Block (RSTBs)**: We adopt a similar experimental setup as that used for the STBs to assess the impact of RSTBs on the performance of LinearTGAN. The results indicate that increasing the number of RSTBs enhances the ability of the model to recover high-frequency details and maintain structural consistency in image reconstruction, as shown in Figure 8. However, beyond eight RSTBs, the performance improvements become negligible, while computational complexity rises significantly. The slight variations observed in LPIPS values beyond this threshold suggest that additional RSTBs offer limited perceptual benefits. These findings highlight the critical role of RSTBs in improving image quality while emphasizing the diminishing returns associated with excessive stacking. Therefore, to achieve a well-balanced compromise between reconstruction accuracy and computational efficiency, we set the number of RSTBs to eight, as this configuration ensures high reconstruction quality while reducing processing costs. **Effect of Residual Connection (RC)**: Figure 8 displays a detailed analysis of the impact of different RC kernel sizes on model performance. An RC facilitates an identity-based pathway between various blocks and the reconstruction module, enabling the effective aggregation of multi-level features. The results indicate that the \(3\times 3\) kernel configuration achieves an optimal balance, effectively preserving fine details and enhancing perceptual quality.