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

Researchers propose an additive causal construction (ACC) framework for multi-source image fusion that addresses cross-system discrepancy and entanglement, improving out-of-distribution generalization. The method, validated on synthetic and real-world medical imaging tasks, establishes causal anchors and uses uncertainty quantification to enhance robustness.

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 additive causal construction ACC framework, which characterizes information fusion at two levels: firstly, it establishes causal "anchors" shared among multiple systems through intervention consistency to enable causal graph transferability CGT ; and secondly, it formalizes the fusion process as causal construction and models the reliability of constructed paths through uncertainty quantification to ensure causal graph reconfigurability CGR . Building upon this, we revisit the traditional causal representation learning CRL with ACC and propose ACC-CRL as a learnable instantiation of the framework. The method explores joint causal content representations across systems via content-mechanism decoupling, and performs response alignment under shared anchors to mitigate CSD. Furthermore, it incorporates structural uncertainty to adaptively regulate the fusion process, thereby suppressing unstable CSE. We conduct systematic experiments on synthetic data ColorMNIST and real-world multi-center medical imaging tasks microvascular invasion MVI prediction . The results demonstrate that the proposed method significantly improves OOD generalization while maintaining in-distribution ID performance, validating the effectiveness and robustness of the ACC-CRL strategy based on mechanism alignment and uncertainty modeling in open environments.