# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 4 Implementation Details
### Quantitative Experimental Results
#### 4.3.1 Quantitative Results Obtained on the UC Merced Land Use Dataset  
As demonstrated in Table 1, this study evaluates the super-resolution performance and computational efficiency of each model across different scaling factors on the UC Merced Land Use dataset, with the best results highlighted in bold. LSANet consistently outperforms state-of-the-art methods in objective metrics, while maintaining low computational cost and a compact parameter footprint. Specifically, for the \(\times\)2 task, LSANet achieves the highest accuracy with only moderate computation, demonstrating its suitability for real-time or resource-constrained applications. As the upscaling factor increases to \(\times\)3 and \(\times\)4, LSANet exhibits strong scalability, with performance degradation well-controlled compared to competing models. This robustness stems from the network's ability to effectively model both local textures and long-range dependencies through the proposed LSKA module, whose large receptive field and separable structure facilitate efficient context aggregation without substantial computational overhead. Additionally, the residual learning design--based on feature distillation and contrast-aware enhancement--supports fine-grained texture recovery even under high magnification, where spatial details are scarce. As shown in Figure 4, LSANet preserves clearer boundaries and structural consistency across all scaling levels, outperforming prior methods that tend to produce blurred contours or artifacts. These results suggest that LSANet achieves a favorable balance between reconstruction quality and model efficiency, making it a promising solution for high-resolution remote sensing image enhancement in practical applications.  
\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 & 8 & 4.5 & 32.89 & 0.8975 \\ CARN & \(\times\)2 & 16 & 0.3 & 33.18 & 0.9196 \\ IMDN & \(\times\)2 & 694 & 21.3 & 32.19 & 0.8996 \\ RFDN & \(\times\)2 & 534 & 26.4 & 33.55 & 0.9032 \\ DCM & \(\times\)2 & 1842 & 30.2 & 33.52 & 0.9166 \\ LGCNet & \(\times\)2 & 193 & 12.7 & 33.65 & 0.9285 \\ CTN & \(\times\)2 & 349 & 4.2 & 33.50 & 0.9242 \\ FeNet & \(\times\)2 & 423 & 67.8 & 33.65 & 0.9278 \\ HSENet & \(\times\)2 & 5286 & 66.8 & 34.23 & 0.9332 \\
**LSANet** & \(\times\)2 & 527 & 19.5 & **34.33** & **0.9328** \\ \hline SRCNN & \(\times\)3 & 8 & 4.5 & 28.66 & 0.8038 \\ CARN & \(\times\)3 & 17 & 0.8 & 29.09 & 0.8167 \\ IMDN & \(\times\)3 & 706 & 22.1 & 29.19 & 0.8367 \\ RFDN & \(\times\)3 & 584 & 28.7 & 29.47 & 0.8392 \\ DCM & \(\times\)3 & 2258 & 16.3 & 29.52 & 0.8396 \\ LGCNet & \(\times\)3 & 193 & 12.7 & 29.65 & 0.8239 \\ CTN & \(\times\)3 & 349 & 2.6 & 29.44 & 0.8319 \\ FeNet & \(\times\)3 & 430 & 69.2 & 29.85 & 0.8405 \\ HSENet & \(\times\)3 & 5370 & 70.8 & 29.98 & 0.8416 \\
**LSANet** & \(\times\)3 & 539 & 15.8 & **30.08** & **0.8446** \\ \hline SRCNN & \(\times\)4 & 8 & 4.5 & 26.78 & 0.7219 \\ CARN & \(\times\)4 & 20 & 1.1 & 26.93 & 0.7267 \\ IMDN & \(\times\)4 & 721 & 22.5 & 27.10 & 0.7413 \\ RFDN & \(\times\)4 & 598 & 30.8 & 27.28 & 0.7505 \\ DCM & \(\times\)4 & 2175 & 13.0 & 27.22 & 0.7528 \\ LGCNet & \(\times\)4 & 193 & 12.7 & 27.02 & 0.7333 \\ CTN & \(\times\)4 & 360 & 2.6 & 27.41 & 0.7512 \\ FeNet & \(\times\)4 & 438 & 71.5 & 27.65 & 0.7596 \\ HSENet & \(\times\)4 & 5433 & 81.2 & 27.73 & 0.7623 \\
**LSANet** & \(\times\)4 & 558 & 13.6 & **27.79** & **0.7635** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The comparison results were derived from the UC Merced Land Use remote sensing dataset. The top performing result is highlighted in bold.