Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion

arXiv:2607.02572v1 Announce Type: new Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition of multiple causal systems. However, cross-system discrepancy (CSD) and cross-system entanglement (CSE) commonly arise during the fusion process, often leading to significant performance degradation under out-of-distribution (OOD) predictions. To address the CSD and CSE issues, we propose the additiv...