HAMD-RSISR: Hybrid Attention and Multidictionary for Remote Sensing Super-Resolution

Remote sensing image super-resolution (RSISR) aims to enhance low-resolution remote sensing images to restore high-quality details. Currently, methods based on Swin Transformer have achieved remarkable success due to their excellent contextual attention capabilities and moving window mechanism. However, the limitations of this window mechanism make it difficult for the model to achieve cross-window information interaction, resulting in the loss of important complementary information. To address this issue, inspired by the adaptive token dictionary (ATD), we propose a new RSISR method, HAMD–RSISR, and for the first time, introduce the use of a “multidictionary mechanism” to break through the limitations of traditional window mechanisms. Unlike the single-dictionary ATD, our multidictionary mechanism enhances the network’s global context modeling capabilities at both coarse and fine granularities. Specifically, one dictionary is used for preliminary redivision of feature maps, while the other dictionary performs further refinement on the previous dictionary to process feature maps at deeper scales. In addition, we propose a hybrid attention mechanism to further effectively utilize channel, spatial, and frequency information in remote sensing images. Finally, the hybrid attention and multidictionary mechanisms process the input feature maps in parallel and perform feature fusion. Experimental results demonstrate the excellent performance of our HAMD in RSISR. On the CLRS × 2 dataset, it achieves a peak signal-to-noise ratio (PSNR) improvement of approximately 0.44 dB compared to the state-of-the-art RGT in recent years, and on the UC Merced × 4 dataset, it achieves a PSNR improvement of approximately 0.4 dB compared to HAT.