Dual-Modal Prior Semantic Guided Infrared and Visible Image Fusion for Intelligent Transportation System

Infrared and visible image fusion (IVF) plays an important role in intelligent transportation system (ITS). The early works predominantly focus on boosting visual appeal of the fused result, although several recent approaches have tried to combine high-level vision task with IVF, they prioritize the design of cascaded structure to seek unified suitable features and fit different tasks. Thus, they tend to bias toward reconstructing raw pixels without considering the significance of semantic features. Therefore, we propose a novel prior semantic guided image fusion method based on the dual-modality strategy, improving the performance of IVF in ITS. Specifically, to explore the independent significant semantic of each modality, we first design two parallel semantic segmentation branches with a refined feature adaptive-modulation (RFaM) mechanism. RFaM can perceive the features that are semantically distinct enough in each semantic segmentation branch. Then, two pilot experiments based on the two branches are conducted to capture the significant prior semantic of source images, which is then applied to guide the fusion task in the integration of semantic segmentation branches and fusion branch. In addition, to aggregate both high-level semantics and impressive visual effects, we further investigate the frequency response of the prior semantics, and propose a multi-level representation-adaptive fusion (MRaF) module to explicitly integrate low-frequency prior semantic with high-frequency details. Extensive experiments on two public datasets demonstrate the superiority of our method over state-of-the-art fusion approaches. Our method has better performance on four quantitative metrics in fusion task and achieves the highest mIoU in semantic segmentation task.