TFI-Fusion: Hierarchical Triple-Stream Feature Interaction Network for Infrared and Visible Image Fusion

As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of existing methods, this paper proposes the Hierarchical Triple-Feature Interaction Fusion Network (TFI-Fusion). Based on a hierarchical triple-stream feature interaction mechanism, the network achieves high-quality fusion through a two-stage, separate-model processing approach: In the first stage, a single model extracts low-rank components (representing global structural features) and sparse components (representing local detail features) from source images via the Low-Rank Sparse Decomposition (LSRSD) module, while capturing cross-modal shared features using the Shared Feature Extractor (SFE). In the second stage, another model performs fusion and reconstruction: it first enhances the complementarity between low-rank and sparse features through the innovatively introduced Bi-Feature Interaction (BFI) module, realizes multi-level feature fusion via the Triple-Feature Interaction (TFI) module, and finally generates fused images with rich scene representation through feature reconstruction. This separate-model design reduces memory usage and improves operational speed. Additionally, a multi-objective optimization function is designed based on the network’s characteristics. Experiments demonstrate that TFI-Fusion exhibits excellent fusion performance, effectively preserving image details and enhancing feature complementarity, thus providing reliable visual data support for downstream tasks.