Multiscale Window Attention Channel Enhanced for Remote Sensing Image Super-Resolution

Transformer-based methods for remote sensing image super-resolution face challenges in reconstructing high-frequency textures due to the interference from large flat regions, such as farmlands and water bodies. To address these limitations, we propose a channel-enhanced multiscale window attention mechanism, which is designed to minimize the impact of flat regions on high-frequency area reconstruction while effectively utilizing the intrinsic multiscale features of remote sensing images.To better capture the multi-scale features of remote sensing images, we introduce a series of depthwise separable convolution kernels of varying sizes during the shallow feature extraction stage. Experimental results demonstrate that the proposed method achieves superior PSNR and SSIM scores across multiple remote sensing benchmark datasets and scaling factors, validating its effectiveness.