DTWSTSR: Dual-Tree Complex Wavelet and Swin Transformer Based Remote Sensing Images Super-Resolution Network

High-resolution remote sensing images provide crucial data support for applications such as precision agriculture and water resource management. However, super-resolution reconstructions often suffer from over-smoothed textures and structural distortions, failing to accurately recover the intricate details of ground objects. To address this issue, this paper proposes a remote sensing image super-resolution network (DTWSTSR) that combines Dual-Tree Complex Wavelet Transform and Swin Transformer, which enhances the ability of texture detail reconstruction by fusing frequency-domain and spatial-domain features. This model includes a Dual-Tree Complex Wavelet Texture Feature Sensing Module (DWTFSM) for integrating frequency and spatial features, and a Multi-Scale Efficient Channel Attention (MS-ECA) mechanism to enhance attention to multi-scale and global details. In addition, we design a Kolmogorov-Arnold Network based on a branch attention mechanism (AKAN), which improves the model's ability to represent complex non-linear features. During the training process, we investigate the impact of hyperparameters and propose the two-stage SSIM&SL1 loss function to reduce structural differences between images. Experimental results show that DTWSTSR outperforms existing mainstream methods under different magnification factors (×2, ×3, ×4), ranking among the top two in multiple metrics. For example, at ×2 magnification, its PSNR value is 0.64–2.68 dB higher than that of other models. Visual comparisons demonstrate that the proposed model achieves clearer and more accurate detail reconstruction of target ground objects. Furthermore, the model exhibits excellent generalization ability in cross-sensor image (OLI2MSI dataset) reconstruction.