## 3 Experiments
### Comparison with the State-of-the-Art  
To verify the superiority of the proposed method, in this section we compare our method with several state-of-the-art methods, including two GAN-based methods (SRResNet [16] and RCAN [29]), three attention-based methods (SPARNet [32], SISN [33], and IGAN [30]), and two pioneering transformer-based methods (SwinIR [37] and Uformer [28]) on the CelebA and Helen datasets along with real face images. Moreover, bicubic interpolation is applied as the baseline. All models were trained using the same CelebA dataset for fair comparison. Quantitative results are tabulated in Table 5.  
_(1) Comparison on CelebA dataset_: Quantitative comparisons with other methods on the CelebA dataset are shown in Table 5. According to the table, the proposed method outperforms other competitive methods in terms of PSNR, SSIM, and LPIPS, which proves  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline
**Approaches** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** \\ \hline Not Applied & 27.54 & 0.7885 & 0.1873 \\ Only Add & 27.58 & 0.7892 & 0.1838 \\ Only Concat & 27.59 & 0.7895 & 0.1832 \\ Our MFFM & **27.63** & **0.7905** & **0.1797** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of different approaches to the multi-scale feature fusion process.  
\begin{table}
\begin{tabular}{c c|c c c} \hline \hline
**SCAGB-B** & **CMTB** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** \\ \hline \(\times\) & \(\times\) & 27.50 & 0.7877 & 0.1897 \\ \(\times\) & \(\checkmark\) & 27.60 & 0.7902 & 0.1811 \\ \(\checkmark\) & \(\times\) & 27.59 & 0.7899 & 0.1817 \\ \(\checkmark\) & \(\checkmark\) & **27.63** & **0.7905** & **0.1797** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of the components in the proposed SCATM-B.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline
**SCATM-B Numbers** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** \\ \hline
**0** & 27.50 & 0.7877 & 0.1897 \\
**2** & 27.57 & 0.7900 & 0.1823 \\
**4** & **27.63** & **0.7905** & **0.1797** \\
**6** & 27.62 & 0.7903 & 0.1804 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of different SCATM-B numbers in the proposed method.  
that the proposed method has the advantage of recovering accurate and realistic face details. Moreover, we provide some test images from the CelebA dataset for visual comparisons, shown in Figure 5. From the first two samples in Figure 5, it can be seen that our method can better restore nose contours and eye details compared to other state-of-the-art methods, while avoiding unpleasant artifacts. This is due to the combination of SCAGB and CMT, which complement each other and can simultaneously enhance both local facial details and global facial structures. Furthermore, despite all the methods mentioned above achieving satisfactory evaluation metrics on the last sample, the visual results are not very compelling due to the inability to reconstruct eyeglasses. Therefore, adding face identifications in the FSR process that can help to identify lost details such as eyeglasses in the LR image represents a good opportunity for future work.