CWFU: Cross-Domain Wavelet Frequency Upsampling for Pan-Sharpening

While deep learning (DL) methods have achieved significant success in pan-sharpening, existing cross-modal upsampling methods typically adopt a premature fusion strategy, causing low- and high-frequency components to be coupled within the mixed features. To address this frequency-coupling problem, we propose the cross-domain wavelet frequency upsampling (CWFU) method. Specifically, CWFU adopts a decompose-at-source strategy: leveraging wavelet transform to separate panchromatic (PAN) into low-frequency global structures and high-frequency local details before cross-modal fusion, establishing explicit frequency boundaries. Low-frequency components are processed in the Fourier domain by the low-frequency feature fusion (LFFF) module, achieving robust fusion of global structures through adaptive amplitude gating and phase convolution while avoiding high-frequency interference. Subsequently, in the spatial domain, the high-frequency detail injection (HFDI) module injects fine-grained details into high-frequency components, guided by the fused low-frequency features to eliminate low-frequency interference. These enhanced components are then reconstructed through inverse wavelet transform, achieving synergistic enhancement of global and local information. Furthermore, as a lightweight plug-and-play module, CWFU can be seamlessly integrated into various backbone networks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our CWFU, which consistently outperforms state-of-the-art upsampling methods.