IA 2 GNN: Imbalance-Aware Adaptive Graph Construction for Multi-Modal Image Fusion

Image fusion aims to integrate multi-modal data, enhancing information completeness while reducing redundancy. However, image fusion faces the challenge of information imbalance. This imbalance is reflected in both intra-image variations, where different regions exhibit significant differences in information density and importance, and cross-modal inconsistencies, where corresponding regions in different modalities contribute unequally to the fused result. Most existing image fusion methods, such as models based on CNN and window attention mechanism, adopt a fixed approach that collects the same amount of neighboring information for each pixel. However, this fixed approach lacks adaptability to local variations in information density, ultimately limiting fusion performance. To this end, we propose IA2GNN, an imbalance-aware adaptive graph neural network designed for image fusion. To tackle intra- and cross-modal information imbalance, we leverage the flexibility of graph structures to dynamically adjust node connections during feature extraction and fusion. Specifically, we adjust the connectivity of nodes in the graph to allow regions with rich details to establish more connections, thereby enhancing the extraction and preservation of key information in these regions. For less informative regions, we reduce connections to prevent over-modeling. In this way, we optimize information distribution, achieving a balance between detail retention and information integration in the fusion results. Experimental results demonstrate that the proposed model outperforms baseline methods across multiple image fusion tasks, achieving up to +0.058 improvement in SSIM and +0.351 gain in MI.