Dual-Prior-Guided Cross-Mamba Network for Hyperspectral and Multispectral Image Fusion

Recently, hyperspectral and multispectral image fusion algorithms based on local and long-range connectivity learning (e.g., CNNs, Transformer and Mamba) have produced good results. However, designing effective hybrid architectures of CNNs, transformer, and Mamba, guided by rich prior information, that efficiently learn complementary spatial-spectral features in complex hyperspectral and multispectral images, thereby further improving image fusion performance, remains a challenge. In this paper, we propose a dual-prior-guided cross-mamba network (DCMN) for hyperspectral and multispectral image fusion. Specifically, we first introduce an efficient cross-modal adjustment nonlocal module (CANL) with low computational cost to enhance the spatial feature representation of low-resolution hyperspectral images by recalculating attention using multispectral images. We then employ a stack of proposed hybrid CNN-Transformer and Mamba blocks (CTMBs), which integrate multimodal semantic and edge-structure prior information, to learn complementary spatial-spectral features at local, regional, and global scales. Extensive experiments are conducted on three datasets from both indoor and outdoor settings, featuring hyperspectral images from agricultural and urban areas. The results show that the proposed DCMN outperforms recent state-of-the-art hyperspectral image fusion methods across different settings, highlighting its effectiveness for hyperspectral and multispectral image fusion.