Multiscale pansharpening method based on frequency feature guidance

Focused on the issues of blurring effect and spectral distortion in current pansharpening approaches, we propose a multiscale pansharpening method based on frequency feature guidance. Firstly, we extract frequency features using learnable Discrete Wavelet Transform Layers (DWTL), select key frequency features for fusion, and then use Inverse Discrete Wavelet Transform Layers (IDWTL) to transform the fused features back to the spatial domain to guide image reconstruction. Secondly, we employ a multi-scale progressive strategy to reconstruct the fused image, effectively leveraging the spectral and spatial features of the source images at different scales. Additionally, we implement a multiscale reconstruction loss constraint during network training to further enhance fusion accuracy. The superiority of our method is shown by testing results on two datasets, both at reduced and full resolution.