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

作者：Yu Yao, Hengbin Wang, Xiang Gao, Ziyao Xing, Xiaodong Zhang, Yuanyuan Zhao, S. H. Li, Zhe Liu · 发表于：IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 年份：2026 · DOI：10.1109/jstars.2026.3651075 · 被引用次数：1 · 研究领域：Advanced Image Fusion Techniques、Advanced Image Processing Techniques、Remote-Sensing Image Classification

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. Visu...