# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 3 Method  
### Network Architecture  
The LSANet architecture proposed in this study is designed to strike an optimal balance between reconstruction quality and computational efficiency for super-resolution tasks, as illustrated in Figure 1. Building upon the RFDN framework, the network incorporates large separable kernel attention mechanisms to enhance performance. The architecture consists of three main components: shallow feature extraction, deep feature extraction, and image reconstruction. The overall framework shares similarities with both RFDN and IMDN in its structural design.  
Figure 1: Overall architecture of the LSANet network.  
The shallow feature extraction module captures fundamental texture and structural information, establishing a foundational representation for subsequent deep processing. The deep feature extraction stage, integrated with residual connections to shallow features, further enhances the representational depth by refining multi-scale contextual details. The image reconstruction module ensures accurate restoration of spatial details with minimal artifacts, leveraging pixel-wise refinement to preserve edge integrity in remote sensing scenes.  
In the shallow feature extraction stage, a \(3\times 3\) convolution is used to extract preliminary shallow features from low-resolution remote sensing images; the deep feature extraction consists of multiple RLSKFDM, feature fusion components, and long jump connections; the image reconstruction consists of a \(3\times 3\) convolution and a layer of PixelShuffle.  
Assuming that the input low-resolution image is \(I_{LR}\), and the output high-resolution image is \(I_{SR}\), the shallow feature extraction can be expressed as  
\[F_{SF}=H_{3\times 3}(I_{LR}), \tag{1}\]  
where \(F_{SF}\) represents the extracted features, and \(H_{3\times 3}\) represents a single layer of \(3\times 3\) convolution. Subsequently, deeper features are extracted from the initial features \(F_{SF}\) through multiple RLSKFDM blocks:  
\[\left\{\begin{array}{l}F_{1}=H_{R1}(F_{SF})\\ F_{2}=H_{R2}(F)\\ \ldots\\ F_{n}=H_{Rn}(F_{n-1})\end{array}\right., \tag{2}\]  
where \(F_{1,2,\ldots n}\) represents the output of each RLSKFDM, and \(H_{R1,R2,\ldots Rn}\) represents the RLSKFDM of each stage. The output features from each RLSKFDM block are concatenated and fused to form a richer feature representation:  
\[F_{C}=H_{C}(F_{1},F_{2},\ldots,F_{n}), \tag{3}\]  
\[F_{DF}=H_{3\times 3}(H_{HS}(H_{1\times 1}(F_{C})))+F_{SF}, \tag{4}\]  
where \(F_{C}\) represents the output features of each module, \(H_{C}\) represents the Concat connection, \(F_{DF}\) represents the fused features, \(H_{3\times 3}\) represents a single layer \(3\times 3\) convolution, \(H_{1\times 1}\) represents a single layer \(1\times 1\) convolution, and \(H_{HS}\) represents the Hard Swish activation function. In the image reconstruction stage, after a \(3\times 3\) convolution layer and PixelShuffle operation, upsampling is performed to obtain a high-resolution image \(I_{HR}\):  
\[I_{HR}=H_{PS}(H_{3\times 3}(F_{DF})), \tag{5}\]  
where \(H_{PS}\) represents the PixelShuffle operation.