TSFI-fusion: A dual-branch decoupled infrared and visible image fusion network based on transformer and spatial-frequency interaction

Infrared and visible image fusion (IVIF) aims to generate high-quality images by combining detailed textures from visible images with the target-highlight capabilities of infrared images. However, many existing methods struggle to capture both shared and unique features of each modality. They often focus only on spatial domain fusion, such as pixel averaging, while overlooking valuable frequency domain information. This makes it hard to retain fine details. To overcome these limitations, we propose TSFI-Fusion, a dual-branch network that combines Transformer-based global understanding with spatial-frequency detail enhancement. The two branches include a Transformer-based semantic construction branch for capturing global features and a detail enhancement branch utilizing an invertible neural network (INN) and a frequency domain compensation module (FDCM) to integrate spatial and frequency information. We also design a dual-domain interaction module (DDIM) to improve feature correlation across domains and a collaborative information integration module (CIIM) to effectively merge features from both branches. Additionally, we introduce a focal frequency loss to guide the model in learning important frequency information. Experimental results demonstrate that TSFI-Fusion outperforms existing methods across multiple datasets and metrics on the IVIF task. In downstream applications such as object detection, it effectively enhances performance. Furthermore, extended experiments on the MIF task reveal the robust generalization ability of the proposed mechanism across diverse fusion scenarios. Our code will be available at https://github.com/lihaowen0109/TSFI-Fusion.