A lightweight remote sensing image super-resolution reconstruction network is introduced to address issues of deployment complexity, limited feature extraction methods, and insufficient ability to capture edge high-frequency information in existing approaches. First, the proposed method initially employs a lightweight saliency detection module to generate a saliency map that emphasizes crucial information regions. Subsequently, a dynamic routing perception module dynamically selects network paths based on the reconstruction difficulty of image patches and salient sub-patches, which enhances model performance. This module integrates multi-scale atrous separable convolution with an information distillation module that features a dual-edge detection operator attention mechanism. Hence, the proposed method can comprehensively extract remote sensing image features and enhance image detail representation ability. Finally, a dual-path upsampling module minimizes model parameters to enable high-quality remote sensing image reconstruction. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the GeoEye-1 dataset are improved by 0.12 dB and 0.0033, respectively, compared with the saldrn algorithm when the images in the GeoEye-1 dataset are magnified by 4 times, while using fewer parameters and achieving faster speeds, thereby demonstrating its advantages in reconstruction performance and effectiveness.