PASG-Net:Spatial-Guided Frequency Compensation Polarized Attention Fusion Network for Hyperspectral and Multispectral Image Fusion

Hyperspectral and multispectral image fusion (HMIF) aims to integrate the complementary characteristics of both modalities to reconstruct high spatial-resolution hyperspectral images (HR-HSI). In recent years, joint modeling in the spatial and frequency domains has become an effective strategy for enhancing fusion performance. However, existing methods still exhibit limitations in extracting spatial–frequency features and achieving complete and efficient integration of complementary information, which often leads to fused images that fail to maintain spatial–spectral consistency. To overcome these challenges, this paper proposes a Spatial-Guided Frequency Compensation Polarized Attention Fusion Network (PASG-Net), achieving HR-HSI reconstruction by integrating spatial-domain and frequency-domain features. Specifically, the Grouped Spatial Feature Extraction Module (GSFE) employs grouped dense residual learning to capture local features in the spatial domain. The Spatial-guided Frequency Compensation Module (SGFC) is designed based on the observation of “phase similarity and magnitude complementarity”, utilizing spatial priors to generate dynamic weights, achieving magnitude fusion and phase fine-tuning to capture comprehensive global frequency-domain features. The Symmetric Polarized Cross-Attention Module (SPCA) introduces Polarized Linear Cross-Attention (PLCA), explicit positive-negative polarity modeling is added to linear attention, effectively integrating complementary information from both domains while maintaining low computational complexity. Extensive experiments demonstrate that the proposed PASG-Net outperforms the current state-of-the-art methods.