VSS-SpatioNet: a multi-scale feature fusion network for multimodal image integrations

Infrared and visible image fusion (vis-ir) enhances diagnostic accuracy in medical imaging and biological analysis. Existing CNN-based and Transformer-based methods face computational inefficiencies in modeling global dependencies. The author proposes VSS-SpatioNet, a lightweight architecture that replaces self-attention in Transformers with a Visual State Space (VSS) module for efficient dependency modeling. The framework employs an asymmetric encoder-decoder with a multi-scale autoencoder and a novel VSS-Spatial (VS) fusion block for local-global feature integration. Evaluations on TNO, Harvard Medical, and RoadScene datasets demonstrate superior performance. On TNO, VSS-SpatioNet achieves state-of-the-art Entropy (En = 7.0058) and Mutual Information (MI = 14.0116), outperforming 12 benchmark methods. For RoadScene, it attains gradient-based fusion performance ([Formula: see text]=0.5712), Piella's metric ([Formula: see text]=0.7926), and average gradient (AG = 5.2994), surpassing prior works. On Harvard Medical, the VS strategy improves Mean Gradient by 18.7% (0.0224 vs. 0.0198) against FusionGAN, validating enhanced feature preservation. Results confirm the framework's efficacy in medical applications, particularly precise tissue characterization.