# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Ablation Study  
This paper devises several ablation experiments with the aim of validating the efficacy of the proposed module. RFDN is used as the benchmark model, and the results of \(\times\)4 super-resolution reconstruction of different experiments on the UC Merced Land Use validation set are statistically analyzed. The validation set is 105 images randomly selected from the test set. The feature extraction block of this benchmark model is SRB, and the number of residual modules and channels is set to 6 and 48, respectively. The ablation experiment in this section consists of the feature extraction module, the feature processing part in the module, and the model dimension and activation function.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Scale** & **Params/K** & **M-Adds/G** & **PSNR** & **SSIM** \\ \hline SRCNN & \(\times\)2 & 57 & 0.52 & 30.21 & 0.8722 \\ CARN & \(\times\)2 & 1592 & 222.6 & 33.93 & 0.9203 \\ IMDN & \(\times\)2 & 694 & 141.2 & 33.72 & 0.9181 \\ RFDN & \(\times\)2 & 534 & 94.1 & 34.15 & 0.9215 \\ DCM & \(\times\)2 & 2341 & 387.2 & 33.54 & 0.9176 \\ LGCNet & \(\times\)2 & 193 & 12.7 & 33.82 & 0.9197 \\ CTN & \(\times\)2 & 1128 & 178.3 & 34.06 & 0.9224 \\ FeNet & \(\times\)2 & 423 & 67.8 & 33.32 & 0.9156 \\ HSENet & \(\times\)2 & 312 & 48.6 & 33.63 & 0.9187 \\
**LSANet** & \(\times\)2 & 602 & 22.1 & **35.02** & **0.9305** \\ \hline SRCNN & \(\times\)3 & 57.2 & 0.53 & 27.84 & 0.8123 \\ CARN & \(\times\)3 & 1602 & 225.3 & 30.74 & 0.8681 \\ IMDN & \(\times\)3 & 702 & 143.7 & 30.57 & 0.8656 \\ RFDN & \(\times\)3 & 541 & 94.1 & 30.99 & 0.8714 \\ DCM & \(\times\)3 & 2355 & 390.6 & 30.49 & 0.8635 \\ LGCNet & \(\times\)3 & 193 & 12.7 & 30.62 & 0.8698 \\ CTN & \(\times\)3 & 1135 & 180.1 & 30.84 & 0.8721 \\ FeNet & \(\times\)3 & 430 & 69.2 & 30.22 & 0.8608 \\ HSENet & \(\times\)3 & 318 & 49.8 & 30.57 & 0.8668 \\
**LSANet** & \(\times\)3 & 605 & 22.3 & **31.24** & **0.8799** \\ \hline SRCNN & \(\times\)4 & 57.3 & 0.54 & 26.13 & 0.7633 \\ CARN & \(\times\)4 & 1618 & 228.9 & 28.87 & 0.8275 \\ IMDN & \(\times\)4 & 715 & 28.65 & 28.74 & 0.7413 \\ RFDN & \(\times\)4 & 550 & 96.8 & 29.05 & 0.8317 \\ DCM & \(\times\)4 & 2378 & 395.1 & 28.54 & 0.8223 \\ LGCNet & \(\times\)4 & 193 & 12.7 & 28.74 & 0.8286 \\ CTN & \(\times\)4 & 1147 & 1.8 & 28.93 & 0.8332 \\ FeNet & \(\times\)4 & 438 & 71.5 & 28.35 & 0.8189 \\ HSENet & \(\times\)4 & 325 & 51.3 & 28.69 & 0.8242 \\
**LSANet** & \(\times\)4 & 609 & 22.7 & **29.29** & **0.8405** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison results were derived from the NWPU-RESISC45 remote sensing dataset. The top performing result is highlighted in bold.  
Although the ablation study primarily evaluates each component independently, we observe that combining RLSKFDM with the proposed RFEM module consistently yields better results than using either module alone. This suggests a synergistic interaction, where the LSKA-based RLSKFDM provides enhanced global context modeling, while the RFE module focuses on refining locally distilled features. The integration of these modules balances long-range dependency modeling and fine-grained reconstruction, which underpins the improved performance of LSANet.  
Figure 5: SR reconstruction validation results produced using different methods on the NWPU-RESISC45 dataset.