## 4 Experiments
### Ablation Study  
To evaluate the effectiveness of individual components in the proposed DRFAN framework, we conduct a series of ablation experiments focusing on three core modules: SGFN, GAB, and the MLLA block. These experiments aim to quantify each module's contribution to performance and analyze how their interactions influence the model's overall accuracy and efficiency. All experiments are carried out under the same training settings, using architectures with comparable parameter counts to ensure fair and meaningful comparisons. We consider four model variants for ablation. Model A: a baseline architecture using MLLA blocks with standard MLPs, without SGFN or GAB; Model B: replaces MLPs in MLLA blocks with SGFN modules, preserving the overall structure; Model C: adds the GAB module to Model A while retaining MLPs; Model D (Proposed): integrates both SGFN and GAB modules into the network. To further enhance the efficiency of GAB and adapt it to lightweight settings, we incorporate a Top-k token selection strategy within the GAB module. This mechanism allows the model to focus attention computation on the most informative spatial regions, thereby suppressing the quadratic complexity typically associated with attention maps in GAB. The Top-k strategy enables sparse attention with minimal performance loss, aligning with our design goal of achieving a favorable trade-off between accuracy and computational efficiency. Quantitative results of these four model variants are presented in Table 1, with evaluations conducted on five standard super-resolution benchmarks under a fixed \(\times\)4 upscaling factor. (1) Efficacy of SGFN Module  
As shown in Table 1, Model B consistently outperforms the baseline Model A across all datasets, with only a slight increase in parameter count. The most notable gain is observed on Urban100, where the PSNR improves from 25.95 dB to 26.14 dB and the SSIM improves by 0.0052. This highlights the SGFN's strength in structured feature generation and its ability to better capture local geometric and textural patterns. The substitution of MLP with the SGFN proves beneficial in dense residual contexts, reducing channel redundancy and enhancing representational capacity. (2) Efficacy of GAB Module  
The ablation results in Table 1 show that adding the GAB alone (Model C) slightly degrades performance compared to the baseline (Model A), suggesting that the GAB's effectiveness depends on its integration strategy rather than any inherent flaw. As a grid-based attention mechanism, the GAB introduces patch partitioning and spatial reordering, which may misalign with standard MLP processing, especially under lightweight constraints. When the GAB is combined with the SGFN (Model D), the network achieves the best overall performance. This improvement stems from the SGFN's ability to provide structured spatial gating, which complements the GAB by maintaining local consistency and guiding feature fusion after attention redistribution. These findings highlight that the synergy between the GAB and SGFN is crucial--the SGFN not only stabilizes the attention pathway but also enables the GAB to operate effectively within a lightweight architecture. We have revised the manuscript to reflect this context-dependent relationship more accurately. (3) Qualitative Analysis and Error Map Visualization  
To visually support the above observations, we present super-resolution results from models A-D on the Urban100 dataset (e.g., img_005 and img_076) in Figure 5. As seen in the visual comparison, Model D yields sharper edges, clearer line structures, and a better preservation of fine textures compared to the other variants. Additionally, we include error maps in Figure 6 to highlight pixel-wise differences from the ground truth. These maps confirm that Model D minimizes reconstruction errors in structurally complex regions, especially near edges and intersections. In contrast, Models A and C display more dispersed and higher-magnitude error zones.