S2WMamba: A Wavelet-Assisted Mamba-Based Dual-Branch Network For Pansharpening

Pansharpening fuses a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) image to produce a high-resolution multispectral (HRMS) image. A key difficulty is that jointly processing PAN and MS features often entangles spatial detail enhancement with spectral fidelity. To address this feature entanglement, we propose S2WMamba, a framework that disentangles modality-specific frequency information to guide cross-modal fusion. Concretely, unlike global frequency transforms, a localized 2D Haar DWT is applied to PAN features to isolate spatial edges and textures. Concurrently, a novel channel-wise 1D Haar DWT treats each pixel's spectrum as a 1D signal, re-parameterizing it as a shared spectral base plus band-specific variations; operating in this basis limits spectral distortion. The resulting Spatial branch injects wavelet-extracted spatial details into MS features, while the Spectral branch refines PAN features using spectra from the DWT1D process. To fuse these decoupled sub-bands, the two branches exchange information via Mamba-based cross-modulation, which models long-range dependencies across the sub-bands with linear complexity. On WV3, GF2, and QB datasets, S2WMamba matches or surpasses strong baselines (FusionMamba, CANNet, U2Net, PanNet), improving reduced-resolution PSNR by up to 0.23 dB and reaching a Hybrid Quality with No Reference (HQNR) of 0.956 on full-resolution WV3. Ablations show that both branches are necessary and that parallel dual-branch fusion outperforms sequential variants.