FASwinNet: Frequency-Aware Swin Transformer for Remote Sensing Image Super-Resolution via Enhanced High-Similarity-Pass Attention and Octave Residual Blocks

Remote Sensing Image Super-Resolution (RSISR) plays a vital role in enhancing the spatial details and interpretability of satellite imagery. However, existing methods often struggle to recover fine textures and high-frequency information effectively. In this paper, we propose a frequency-aware super-resolution network for remote sensing images, termed FASwinNet. The network introduces an Enhanced High-Similarity-Pass Attention (EHSPA) module, which improves high-frequency detail modeling through a similarity-aware mechanism guided by edge and positional information. Additionally, we design an Octave-based Residual Attention Block that explicitly separates and optimizes high and low-frequency features, further enhancing texture reconstruction. Experimental results demonstrate that FASwinNet outperforms state-of-the-art methods in both visual quality and quantitative metrics, achieving the best PSNR and SSIM performance on the AID and UCMerced datasets.