Interpretable General Image Fusion via Scalable Autoregressive Modeling

Existing image fusion methods have developed increasingly sophisticated network architectures for exploiting modality-shared and modality-specific features. However, despite these advancements in feature extraction, most methods ultimately rely on relatively simple implicit or explicit fusion strategies, which can compromise interpretability and limit fusion accuracy. In this paper, we incorporate visual autoregressive modeling to bridge the gap between implicit feature extraction and explicit modality fusion. First, the proposed approach conducts a low-to-high resolution autoregressive objective with modality-specific features, introducing a scalable feature autoregressive mechanism. It aggregates local and global contextual dependencies while enhancing implicit cross-scale interaction. Furthermore, to promote the consistency and complementarity across modalities, we embed an explicit high-order fusion strategy within the progressive modality-specific feature extraction process. This integration facilitates a next-scale synergistic relationship between implicit learning and explicit fusion. Our High-order Feature AutoRegressive Fusion framework (HFARFusion) provides a robust and interpretable solution for general image fusion tasks, effectively balancing fusion performance and transparency through the strengths of autoregressive learning. Extensive experiments demonstrate the outstanding performance of the proposed method in several classical fusion tasks, including infrared-visible, medical, multi-focus, and multi-exposure image fusion. Our code is available at https://github.com/happysbn/HFARFusion.