# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 2 Related Works
### Single Image Super Resolution  
The single image super-resolution task aims to reconstruct a high-resolution image from its low-resolution counterpart. In recent years, deep-learning-based methods have made remarkable progress in this field, evolving from early shallow convolutional networks to deeper architectures and gradually integrating attention mechanisms and Transformer frameworks to enhance both reconstruction quality and computational efficiency. Initially, SRCNN [3] pioneered the use of a three-layer convolutional neural network to learn the LR-to-HR mapping, setting a precedent for deep learning in super resolution. Subsequently, VDSR [4] mitigated the gradient vanishing issue through deep residual learning and leveraged a large receptive field to improve reconstruction performance. EDSR [6] further enhanced model representational capability by removing batch normalization layers. Many studies [26; 27; 28; 29] incorporated attention mechanisms into the EDSR framework to boost performance. Considerable research has focused on optimizing network block designs and feature propagation strategies--for instance, dense connections have been proven effective in enhancing feature expressiveness for SISR tasks [30; 31; 32]. SRDenseNet [33] introduces a dense connection structure that realizesefficient feature reuse and alleviates the problem of gradient disappearance. However, the use of dense connections leads to an increase in computational overhead, making it unsuitable for lightweight requirements. To further reduce the computational overhead, some studies have introduced depthwise separable convolution and grouped convolution structures [34, 35, 36]. DRCN [37] and DRRN [38] adopt a recursive learning strategy to reduce the number of parameters by sharing weights. However, these methods overlook the demand for high-resolution details of complex ground objects in remote sensing images, such as urban building clusters, resulting in semantic ambiguity in the reconstruction results. LapSRN [39] uses a progressive upsampling mechanism to improve inference efficiency that overlooks the multi-scale feature hierarchy critical for remote sensing images; this limitation leads to blurred boundaries in high-magnification SR tasks. [PERSON] et al. [40] proposed Vision Transformers based on structural re-parameterization, demonstrating innovations in model lightweighting but inadequately modeling remote-sensing-specific texture and edge details. [PERSON] et al. [41] presented a context-aware lightweight remote sensing image super-resolution network, yet it still has room for enhancement in multi-scale feature fusion and performance balance. Moreover, the Transformer architecture has attracted significant attention in the super-resolution field due to its outstanding ability to model long-range dependencies. Although the above-mentioned methods have made remarkable progress in performance, they still encounter significant challenges in scenarios with limited computing resources. Therefore, designing a more efficient network architecture to further reduce computational complexity while maintaining high reconstruction quality remains a key research topic in the field of super resolution. Our LSANet tackles this challenge by incorporating hierarchical skip connections into the RFEM, facilitating the seamless integration of shallow-scale texture features and deep-scale semantic features. This design showcases enhanced multi-scale feature integration capabilities, specifically adapted to the complex multi-resolution characteristics of remote sensing scenes.