# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 1 Introduction
Inspired by this, some researchers have attempted to introduce large-kernel convolution into SR tasks. For example, large kernel attention (LKA) [15] boosts the capability to model long-range dependencies via the large kernel attention mechanism. For remote sensing scenarios, existing lightweight SR networks face critical limitations: First, they lack sufficient capability to model intricate details like complex textures and edges in remote sensing images, failing to meet the de mand for fine-grained information in remote sensing interpretation. Second, their network architectures are inefficient in fusing multi-scale and heterogeneous features of remote sensing data, which hinders feature representation and utilization. Third, the balance between lightness and performance improvement is suboptimal; in practical remote sensing deployment scenarios such as edge computing, the computational and storage costs still need optimization. Thus, designing an efficient, lightweight remote sensing image super-resolution network with long-range information modeling capability has become a crucial problem that demands immediate solution. Building upon existing lightweight module architectures, this paper proposes a large separable kernel attention network (LSANet). The model enhances the RFDN framework by replacing the original SRB with the large separable kernel attention (LSKA) module, which significantly expands the network's receptive field and strengthens its capability to capture both global and local contextual information. Additionally, a novel residual large kernel separable feature distillation module is designed, integrating contrast-aware channel attention, LSKA, and multi-layer skip connections to further optimize feature extraction and fusion efficiency. The primary contributions of this model are as follows:  
1. Breaking through module design bottlenecks: The large separable kernel attention mechanism (LSKA) is innovatively introduced into the feature distillation network, and the residual feature distillation module (RLSKPDM) is proposed. By constructing a large-receptive-field attention mechanism, the modeling of complex textures and edge information in remote sensing images is strengthened, breaking through the limitation of insufficient representation ability of shallow residual blocks and providing a more effective feature processing path for the detail restoration of remote sensing images;
2. Innovation in multi-dimensional feature enhancement: A residual feature enhancement module (RFEM) that fuses multiple attention mechanisms is designed. It integrates the contrast-aware channel attention (CCA) and the large separable kernel attention (LSKA) and combines with a multi-level skip connection structure. It accurately strengthens local feature expression and multi-scale information fusion, realizes the efficient coupling of shallow details and deep semantics, and meets the interpretation requirements of multi-dimensional features of remote sensing images;
3. Synergistic optimization of lightness and performance: While improving the reconstruction performance, through the design of lightweight modules and the optimization of the feature flow mechanism, LSANet can greatly reduce the number of parameters and computational overhead while maintaining a competitive restoration effect. An efficient model suitable for edge computing and low-resource remote sensing scenarios is created, solving the \"performance - cost\" imbalance problem of lightweight networks in the actual application of remote sensing;
4. Verification for remote sensing scenarios: Systematic experiments are carried out on two representative remote sensing image datasets, NWPU-RESISC45 and UC Merced Land Use. The advantages of LSANet in image reconstruction quality, model efficiency, and visual performance are fully verified. Covering typical application data scenarios of remote sensing, it fully demonstrates the generality and practical value of the model and provides strong technical support for the implementation of remote sensing super resolution;