## 5 Experimental Results  
### RQ1: How Effective Is LinearTGAN in Enhancing Linear Acoustic Image Quality?  
#### 5.1.1 Quantitative Results  
Table 1 presents the average quantitative results across multiple evaluation metrics. Detailed results for each wafer are provided in Supplementary Figure S1. These findings provide a comprehensive assessment of the effectiveness of various SR approaches in reconstructing high-quality images. Bicubic interpolation remains a widely used and computationally efficient baseline for SR evaluation. However, due to the specificity of our dataset, it achieves a PSNR of 27.341 dB and an SSIM of 0.848, which are significantly lower than those obtained by deep learning-based methods. This highlights its inherent limitations in accurately recovering details and maintaining structural coherence, often leading to blurred artifacts and degraded perceptual quality in reconstructed images.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Type** & **Model** & **Params** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **LPIPS (AlexNet) \(\downarrow\)** & **LPIPS (VGG) \(\downarrow\)** & **FID \(\downarrow\)** \\ \hline Image processing & Bicubic & - & 27.341 & 0.848 & 0.208 & 0.223 & 0.999 \\ \hline CNN-Based & CARN & 1,591,939 & 28.887 & 0.873 & 0.101 & 0.195 & 2.493 \\ \hline GAN-Based & BSRGAN & 16,661,059 & 26.599 & 0.793 & 0.200 & 0.293 & 3.148 \\  & Real-ESRGAN & 16,703,171 & 22.808 & 0.520 & 0.423 & 0.515 & 75.122 \\ \hline Transformer & TransENet & 37,311,195 & 26.437 & 0.772 & 0.225 & 0.320 & 0.824 \\ \hline GAN+Transformer & LinearTGAN (Ours) & 16,129,284 & 29.479 & 0.874 & 0.095 & 0.182 & 0.445 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation of models across performance metrics. The best and second-best results are indicated in **red** and **blue**, respectively. \(\uparrow\) Higher score is better, and \(\downarrow\) Lower score is better.  
Our proposed LinearTGAN model demonstrates significant improvements over the compared methods, achieving the highest PSNR of 29.479 dB and the best SSIM of 0.874. This superior performance underscores its ability to effectively reconstruct fine textures, recover high-frequency details, and maintain the structural integrity of acoustic images. Additionally, LinearTGAN obtains the lowest LPIPS scores (0.095 with AlexNet and 0.182 with VGG), indicating a higher degree of perceptual similarity to the ground truth compared to both CNN-based and transformer-based models. Furthermore, the FID score of 0.445, which is substantially lower than those of competing methods, reinforces the realism and fidelity of the generated images.  
The CNN-based CARN model achieves a competitive PSNR of 28.887 dB and an SSIM of 0.873, reflecting strong performance among conventional deep architectures. However, its limitations in artifact suppression and texture consistency reduce its effectiveness in high-quality image reconstruction. GAN-based models like BSRGAN and Real-ESRGAN show lower perceptual quality, with elevated LPIPS and FID scores due to sensitivity to real-world degradations. The transformer-based TransENet exhibits moderate performance (PSNR of 26.437 dB and SSIM of 0.772), highlighting challenges in generalizing across diverse structures. Figure 3 illustrates the PSNR and FID relative to model complexity, with additional metrics in Supplementary Figure S2. These results underscore the superior reconstruction capability of LinearTGAN, which outperforms CNN, GAN, and Transformer-based models in pixel-level accuracy and perceptual quality, establishing it as an SOTA approach for high-fidelity image SR.