# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 1 Introduction  
Remote sensing imagery constitutes the fundamental data source for geospatial analysis. High-resolution observations, in particular, have proven highly valuable across diverse domains, including ecosystem assessment, land use mapping, and smart citydevelopment [1]. In the realm of remote sensing, satellite-captured imagery often suffers from low resolution. This is attributable to influences like orbital distance, atmospheric interference, and satellite motion during data acquisition [2], posing substantial hurdles for subsequent interpretation tasks. While upgrading satellite imaging hardware could enhance resolution, such an approach incurs exorbitant costs. In contrast to hardware overhauls requiring massive capital outlays, super resolution offers a more economical means of obtaining high-spatial-resolution remote sensing data. In recent years, deep learning has achieved remarkable advancements in the domain of super-resolution tasks. Notably, convolutional neural networks, leveraging their powerful feature-learning capabilities, have seen widespread adoption across diverse fields such as remote sensing, medical imaging, and computer vision. [PERSON] et al. pioneered the super-resolution convolutional neural network (SRCNN) [3], enabling end-to-end super-resolution reconstruction within a three-layer shallow CNN architecture. Relative to traditional methods rooted in interpolation or dictionary learning, its reconstruction quality witnessed a marked enhancement. Subsequently, [PERSON] et al. introduced the very deep super resolution (VDSR) [4], which further bolstered the performance of SR tasks by deepening the network to 20 layers and incorporating residual learning. GoogLeNet [5] devised a multi-scale Inception network structure, employing convolution kernels of varying sizes to extract features from images of different dimensions, thus making efficient use of computational resources. However, its generalization ability is constrained, implying that for specific tasks, tailored adjustments are requisite. Nevertheless, the exponential rise in computational complexity and parameter count inherent to deep networks has emerged as a pivotal factor impeding their broader application. In order to improve the expressiveness of SR models and reduce computational costs, researchers have explored network structure optimization: for example, enhanced deep residual networks (EDSR) [6] achieved notable performance gains on benchmark datasets by removing batch normalization layers and increasing network depth, while residual dense network (RDN) [7] adopted a dense connection strategy to fully reuse features across different layers and facilitate information flow transmission. Very deep residual channel attention networks (RCAN) [8] further introduced channel attention based on the RDN structure, enabling adaptive adjustment of channel weights to enhance the model's focus on high-frequency detail information. However, the architectural design of these models involves a large number of parameters, leading to excessively high computational overhead in practical deployment scenarios. To address this, lightweight SR networks have emerged as a key research direction in recent years: CARN [9] employed a recursive architecture and cascade feature refinement to reduce parameter counts without compromising reconstruction quality, while the information multi-distillation network (IMDN) [10] significantly cut down on computation by grouping features and fusing information through multiple approaches--still delivering performance competitive with RDN. The residual feature distillation network (RFDN) [11] further optimized IMDN's structure to boost inference speed and parameter utilization, and MAFFSRN [12] reduced computational redundancy via feature filtering to lower complexity while retaining high accuracy. In computer vision, large-kernel convolution has attracted extensive attention, with designs like RepLKNet [13] and ConvNeXt [14] effectively reducing computational complexity while preserving model expressiveness.