Lightweight Edge-Guided Super-Resolution Network for Remote Sensing Images

Recently, deep learning-based remote sensing image super-resolution (RSISR) techniques have achieved significant progress, but challenges remain in preserving critical edge details essential for high-quality image reconstruction, These details are crucial for tasks like object recognition, change detection, and accurate analysis in remote sensing imagery. Furthermore, existing RSISR methods typically require substantial computational resources, making them unsuitable for resource-constrained edge devices. To address these challenges, we propose a novel Edge-Guided Super-Resolution Network (EGSRN). The network employs an Edge Extraction Module (Edge Net) to explicitly extract edge information from low-resolution images, combined with multi-layer Feature Extraction Modules (FEM) and an Edge Information Fusion (EIF) mechanism to progressively integrate edge and image features. This design enables precise recovery of edge details, significantly enhancing the overall visual quality of the reconstructed images. Edge-aware processing enhances visual fidelity while also improving the accuracy of downstream tasks, such as classification, object detection, and change analysis. Furthermore, the network incorporates lightweight designs such as depthwise separable convolutions and channel shuffling to effectively reduce computational demands. Comprehensive experiments were conducted on two remote sensing datasets, and the model’s parameter count and floating-point operations (FLOPs) were evaluated. Results demonstrate that the proposed method achieves an excellent balance between performance and model complexity, delivering superior super-resolution reconstruction quality while maintaining low computational costs, making it well-suited for resource-limited real-world applications.