DBCTfuse: A Dual-Branch CNN-Transformer feature extraction network for Infrared and Visible Image Fusion

The process of fusing infrared and visible images necessitates integrating thermal radiation information from infrared image with the edge and texture detail captured by visible images. Most current fusion methods are based on convolutional neural networks, which can effectively extract local features from source images but are somewhat lacking in global features. To address this, we propose a dual-branch feature extraction fusion network based on convolutional neural network and Transformer—DBCTfuse, to effectively synthesize both local details and global contextual information from source images. Comprehensive experiments conclusively demonstrate the superiority of our DBCTfuse method in infrared and visible image fusion, surpassing contemporary state-of-the-art algorithms in both qualitative and quantitative assessments.