## 4 Experiments
### Ablation Studies  
To assess the effectiveness of individual model modules, we conducted a series of ablation studies on the CelebA test sets for \(\times 8\) SR.  
**(1) Study on AGTM-T**: AGTM-T is a module that combines an AGB-T and a CMTB to extract and promote both local facial details and global facial structures. This module marks the first attempt to explore the potential of inner feature map information with a guiding block to reconstruct plausible face images in the transformer-based FSR area. To test its effectiveness, we design three test models by removing different module parts, of which the results are shown in Table 1.  
From the table, we can observe that:  
(a) The performance decreases dramatically when the AGTM-T module is completely removed. The proposed model structure will be considerably shallower without the AGTM-T, which further results in the difficulty in refining input features. Moreover, the multi-scale feature fusion operation module (MFFM), which is complemented with the AGTM-T, will also be greatly affected. (b) The AGTM-T with a single component performs better compared with the no-component one mentioned above. This demonstrates that both AGB-T and CMTB benefit the learning ability of the proposed model. However, the AGTM-T with AGB-T only lost the guiding target, while the AGTM-T with CMTB only cannot focus on crucial feature parts, limiting its performance. (c) The carefully designed components AGB-T and CMTB ensure that the AGTM-T achieves the best performance in all evaluation matrices. This proves that the AGB-T and CMTB are complementary and can simultaneously enhance local facial details and global facial structures. **(2) Study on AGTM-B**: AGTM-B, which contains an AGB-B and a CMTB, aims to enhance the low-level encoded features. Similar experiments as the above section are conducted and the results are shown in Table 2. We have arrived at comparable observations and conclusions in the preceding AGTM-T section. However, we do notice that the performance of the model without AGTM-B is better than that without AGTM-T. This is because AGTM-T and MFFM are more complementary when compared to the relationship between AGTM-B and MFFM. The removal of AGTM-T will result in a further decline in the performance of the proposed method. Furthermore, we also conduct an evaluation of the model on the number of AGTM-Bs, whose results are shown in Table 3. It can be observed that the performance of the model \begin{table}
\begin{tabular}{c c|c c c} \hline \hline
**AGB-T** & **CMTB** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **LPIPS\(\downarrow\)** \\ \hline \(\times\) & \(\times\) & 27.45 & 0.7870 & 0.2026 \\ \(\times\) & ✓ & 27.68 & 0.7925 & 0.1804 \\ ✓ & \(\times\) & 27.71 & 0.7931 & 0.1795 \\ ✓ & ✓ & **27.77** & **0.7941** & **0.1766** \\ \hline \hline \end{tabular} The symbols \(\surd\) and \(\times\) indicate whether or not a corresponding block is included, respectively. The **best** results are emphasized with **bold**. \(\uparrow\) and \(\downarrow\) indicate whether higher or lower evaluation matrix values correspond to better image quality, respectively.  
\end{table} 
Table 1: Ablation study of the components in the proposed AGTM-T.  
is poor without any AGTM-B, suggesting that AGTM-B plays a crucial role in the model. Meanwhile, we also notice that the performance improves when the number of AGTM-Bs increases within a specific range. However, when the number of AGTM-Bs exceeds 4, the change rate of the evaluation matrix slows down, and the performance even decreases slightly. Therefore, to maintain a good balance between model size and performance, we set the number of AGTM-Bs to 4. _(3) Study on MFFM_: MFFM is specially designed to integrate features from all layers to improve network flexibility and restoration performance.