Enhanced Spatial-Frequency Synergistic Network for Multispectral and Hyperspectral Image Fusion

Multispectral and hyperspectral image fusion (MHIF) seeks to combine high-resolution multispectral images (HR-MSIs) with low-resolution hyperspectral images (LR-HSIs) to create high-resolution hyperspectral images (HR-HSIs). Transformer-based architectures have recently become prominent in MHIF tasks due to their effective global self-attention mechanisms. However, the quadratic computational complexity of the global self-attention in Transformers presents significant challenges for practical applications. In this paper, we propose an enhanced spatial-frequency synergistic (ESFS) approach that leverages both spatial and frequency domain features to enhance fusion quality. Our ESFS framework introduces the condensed spatial augmentation module (CSAM), which condenses window features and employs cross-attention to balance extensive contextual understanding and detailed local feature extraction while reducing computational overhead. Additionally, we develop the selective frequency decomposition module (SFDM), which utilizes global filters composed of phase and amplitude information in the frequency domain to retain features, effectively capturing deep frequency domain characteristics and their interdependencies. Comprehensive experiments on three benchmark MHIF datasets demonstrate that our method achieves superior performance, establishing a new state-of-the-art (SOTA) in both quantitative metrics and visual quality assessments. The code is available at http://szu-hsilab.com/.