Semantic Segmentation-Driven Knowledge Distillation-Based Infrared Visible Image Fusion Framework

The goal of infrared and visible image fusion is to generate a fused image that integrates both prominent targets and fine textures. However, many existing fusion algorithms overly emphasize visual quality and traditional statistical evaluation metrics while neglecting the requirements of real-world applications, especially in high-level vision tasks. To address this issue, this paper proposes a semantic segmentation-driven image fusion framework based on knowledge distillation. By incorporating a distributed structure of teacher and student networks, the framework leverages knowledge distillation to reduce network complexity, ensuring that the fused images are not only visually enhanced but also well-suited for downstream high-level vision tasks. Additionally, the introduction of two discriminators further optimizes the overall quality of the fused images, while the integration of a semantic segmentation module ensures that the fused images provide valuable support for advanced vision tasks. To enhance both fusion performance and segmentation capability, this paper proposes a joint training strategy that enables the fusion and segmentation networks to mutually improve during training. Experimental results on three public datasets demonstrate that the proposed method outperforms nine state-of-the-art fusion approaches in terms of visual quality, evaluation metrics, and semantic segmentation performance. Finally, ablation studies on the segmentation network further validate the effectiveness of the proposed method.