付丁丁, Zhongguo Li, Wenbin Fan, Qi Wang
Infrared-visible image fusion aims to integrate structural details, natural appearance, and thermal target information from two source modalities, thereby improving visual perception in complex scenes. However, under challenging conditions such as low illumination, noise, low contrast, and overexposure, existing methods often struggle to stably preserve cross-modal shared features (CMSF) while effectively highlighting single-modal specific features (SMSF). In addition, the absence of real fusion labels limits effective supervised learning. To address these issues, this paper proposes a unified semantic-guided fusion network, termed USF-Net, which jointly models the shared and specific features of infrared and visible images under a unified semantic representation and dynamically adjusts the fusion strategy according to imaging contexts. Specifically, the Shared Feature Alignment and Enhancement (SFAE) module is designed to strengthen consistent modeling of common features across modalities, while the Specific Feature Reweighting Fusion (SFRF) module selectively enhances modality-specific features to achieve stable and controllable fusion. Moreover, the constructed real fusion labels are incorporated into the loss function for collaborative training. Experimental results on multiple public datasets demonstrate that USF-Net achieves superior fusion performance under diverse complex imaging conditions.