## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.1 Quantitative Comparison  
As shown in Table 1, our method, using only the reconstruction loss, achieves state-of-the-art results on the three benchmark datasets: Sun80, Urban100, and Manga109. For the more complex datasets CUFED5 and WR-SR, our model also demonstrates exceptional generalization ability, outperforming most of the comparison methods and achieving better results than the baseline method C\({}^{2}\)-Matching. Our method leverages effective adaptive learnable texture matching and dynamic texture transfer during reconstruction, which allows similar textures from high-resolution reference images in the dataset to be transferred to low-resolution images, thereby enhancing high-frequency information. This adaptive transfer strategy effectively addresses the misalignment issue in texture transfer found in traditional methods, significantly improving detail restoration while maintaining structural consistency. According to the results in Table 2, after introducing perceptual loss and adversarial loss for joint supervision, our model still shows significant advantages. Despite the trade-off effect between different loss functions causing some fluctuation in quantitative metrics, our method remains superior in most comparison scenarios. Through quantitative comparisons under both paradigms, our architecture demonstrates excellent robustness. Its stable performance across different supervision modes verifies the versatility of the method's design and achieves optimal performance. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**CUFED5 [3]**} & \multicolumn{1}{c}{**Sun80 [34]**} & \multicolumn{1}{c}{**Urban100 [35]**} & \multicolumn{1}{c}{**Manga109 [36]**} & \multicolumn{1}{c}{**WR-SR [6]**} \\ \cline{3-6}  & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** & **PSNR/SSIM** \\ \hline \multirow{5}{*}{SISR} & SRCNN [10] & 25.33/0.745 & 28.26/0.781 & 24.41/0.738 & 27.12/0.850 & 27.27/0.767 \\  & EDSR [12] & 25.93/0.777 & 28.52/0.792 & 25.51/0.783 & 28.93/0.891 & 28.07/0.793 \\  & RCAN [15] & 26.06/0.769 & 29.86/0.810 & 25.42/0.768 & 29.38/0.895 & 28.25/0.799 \\  & SRGAN [16] & 24.40/0.702 & 26.76/0.725 & 24.07/0.729 & 25.12/0.802 & 26.21/0.728 \\  & Enet [37] & 24.24/0.695 & 26.24/0.702 & 23.63/0.711 & 25.25/0.802 & 25.47/0.699 \\  & ESRGAN [18] & 21.90/0.633 & 24.18/0.651 & 20.91/0.620 & 25.53/0.797 & 26.07/0.726 \\  & RankSRGAN [19] & 22.31/0.635 & 25.60/0.667 & 21.47/0.624 & 25.04/0.803 & 26.15/0.719 \\ \hline \multirow{5}{*}{RefSR} & CrossNet [1] & 25.48/0.764 & 28.52/0.793 & 25.11/0.764 & 23.36/0.741 & 23.77/0.612 \\  & SRNTT-rec [3] & 26.24/0.784 & 28.54/0.793 & 25.50/0.783 & 28.95/0.885 & 27.59/0.780 \\  & TTSR-rec [4] & 27.09/0.804 & 30.02/0.814 & 25.87/0.784 & 30.09/0.907 & 27.97/0.792 \\  & SSEN-rec [2] & 26.78/0.791 & - & - & - & - \\  & MASA-rec [5] & 27.54/0.814 & 30.15/0.815 & 26.09/0.786 & 30.28/0.909 & 28.19/0.796 \\  & C\({}^{2}\)-Matching-rec [6] & 28.24/0.841 & 30.18/0.817 & 26.03/0.785 & 30.47/0.911 & 28.32/0.801 \\  & RSRR-rec [26] & **28.83/0.856** & 30.13/0.816 & 26.21/0.790 & 30.91/0.913 & 28.41/**0.804** \\  & Ours-rec & 28.62/0.853 & **30.25/0.819** & **26.29/0.793** & **31.02/0.915** & **28.49/**0.802** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison between SISR model and RefSR model (using only reconstruction loss, suffix “-rec”). The best and second best PSNR/SSIM values are marked in bold and underlined in the table, respectively.