SFSIN: A Lightweight Model for Remote Sensing Image Super-Resolution with Strip-like Feature Superpixel Interaction Network

Remote sensing image (RSI) super-resolution plays a critical role in improving image details and reducing costs associated with physical imaging devices. However, existing super-resolution methods are not applicable to resource-constrained edge devices because they are hampered by a large number of parameters and significant computational complexity. To address these challenges, we propose a novel lightweight super-resolution model for remote sensing images, a strip-like feature superpixel interaction network (SFSIN), which combines the flexibility of convolutional neural networks (CNNs) with the long-range learning capabilities of a Transformer. Specifically, the Transformer captures global context information through long-range dependencies, while the CNN performs shape-adaptive convolutions. By stacking strip-like feature superpixel interaction (SFSI) modules, we aggregate strip-like features to enable deep feature extraction from local and global perspectives. In addition to traditional methods that rely solely on direct upsampling for reconstruction, our model uses the convolutional block attention module with upsampling convolution (CBAMUpConv), which integrates deep features from spatial and channel dimensions to improve reconstruction performance. Extensive experiments on the AID dataset show that SFSIN outperforms ten state-of-the-art lightweight models. SFSIN achieves a PSNR of 33.10 dB and an SSIM of 0.8715 on the ×2 scale, outperforming competitive models in both quantity and quality, while also excelling at higher scales.