TEDFuse: Task-Driven Equivariant Consistency Decomposition Network for Multi-Modal Image Fusion

Multimodal image fusion integrates infrared and visible images by leveraging their complementary strengths. However, most existing fusion techniques primarily focus on pixel level integration, often neglecting the preservation of semantic consistency between the source and fused images. To address this limitation, we propose TEDFuse, a Task-Driven Equivariant Consistency Decomposition Network that ensures semantic con sistency within the image space and across high-level semantic tasks. TEDFuse incorporates two key components: first, a robust decomposition framework with equivariant consistency, ensuring that the fused image retains consistent transformation properties under shifts, rotations, and reflections, thereby enhancing local detail preservation and global semantic alignment; In addition, a task-driven fusion framework that integrates a segmentation module, reinforcing semantic feature preservation through a semantic loss function and ensuring consistency in downstream tasks such as segmentation and detection. The proposed method not only preserves the semantic coherence of the fused image but also improves performance in high-level tasks, demonstrating superior capability in multimodal fusion for complex visual applications. Extensive experiments validate the effectiveness of TEDFuse by analyzing feature evolution, examining the relationship between fusion quality and task performance, and discussing calibration strategies for infrared-visible image fusion. The code is available at https://github.com/Claire-cxy/TEDFuse.