用小波分解分离多光谱与全色图像特征，提升融合清晰度与保真度。
S2WMamba: A Wavelet-Assisted Mamba-Based Dual-Branch Network For Pansharpening
- 分频处理：全色图用二维小波提取边缘，多光谱图用一维小波分离波段共性与差异
- 双分支并行：空间分支与光谱分支独立优化，减少特征混淆，提升融合质量
- 基于Mamba的跨模态交互：线性复杂度建模长程依赖，适合高分辨率遥感图像
全色锐化旨在将高分辨率全色（PAN）图像与低分辨率多光谱（LRMS）图像融合，生成高分辨率多光谱（HRMS）图像。关键难点在于联合处理时空间细节增强与光谱保真常相互干扰。为此，本文提出S2WMamba框架，通过显式解耦模态特异性频率信息实现可控的跨模态交互。具体地，对全色图采用局部2D Haar DWT精准分离空间边缘与纹理；同时，将每个像素的光谱视为1D信号，利用新型通道级1D Haar DWT分离共享光谱基底与波段特异性变化，严格限制光谱失真。由此，光谱分支将小波提取的空间细节注入多光谱特征，空间分支则利用1D小波所得光谱信息优化全色特征。为克服频率融合不足，两分支通过Mamba-based交叉调制交换信息，以线性复杂度显式建模解耦子带间的长程依赖。在WV3、GF2和QB数据集上，S2WMamba达到或超越近期强基线（FusionMamba、CANNet、U2Net、PanNet），PSNR最高提升0.23 dB，全分辨率WV3上取得HQNR 0.956。大量消融实验验证了模态特异性小波位置与并行双分支结构的有效性。
原文摘要 · Abstract (English)
Pansharpening fuses a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) 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 explicitly disentangles modality-specific frequency information for highly controlled crossmodal interaction. Concretely, unlike global frequency transforms, a localized 2D Haar DWT is applied to the PAN image to precisely isolate spatial edges and textures. Concurrently, a novel channel-wise 1D Haar DWT treats each pixel's spectrum as a 1D signal, isolating the shared spectral base from band-specific variations to strictly limit spectral distortion. The resulting Spectral branch injects wavelet-extracted spatial details into MS features, while the Spatial branch refines PAN features using spectra from the DWT1D process. To overcome inadequate frequency fusion, the two branches exchange information via Mambabased cross-modulation, which explicitly models long-range dependencies across these decoupled sub-bands with linear complexity. On WV3, GF2, and QB datasets, S2WMamba matches or surpasses recent strong baselines (FusionMamba, CANNet, U2Net, PanNet), improving PSNR by up to 0.23 dB and reaching an HQNR of 0.956 on full-resolution WV3. Extensive ablations justify the modality-specific DWT placement and the parallel dual-branch architecture.
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