用因果分析解决红外与可见光图像融合中的偏见问题。
Causality-Driven Infrared and Visible Image Fusion
- 从因果视角构建图像融合的因果图，分离干扰因素。
- 提出BAFFM模块，消除混淆变量影响，提升融合效果。
- 在三个标准数据集上超越现有方法，适合多模态融合研究者。
图像融合旨在整合多源图像的互补信息，生成更完整的场景表征。现有方法主要依赖网络结构堆叠与设计来提升融合性能，却常忽略数据集场景偏见对模型训练的影响。这种忽视导致模型在传统似然估计框架下学习到特定场景与融合权重间的虚假关联，从而限制了融合表现。本文首次从因果视角重新审视图像融合任务，通过构建定制因果图，厘清图像融合中各变量间的因果关系，使模型摆脱偏见影响。进而提出基于后门调整的特征融合模块（BAFFM），消除混淆因子干扰，使模型能够学习真实的因果效应。在三个标准数据集上的大量实验表明，所提方法在红外与可见光图像融合任务中显著优于现有最先进方法。
原文摘要 · Abstract (English)
Image fusion aims to combine complementary information from multiple source images to generate more comprehensive scene representations. Existing methods primarily rely on the stacking and design of network architectures to enhance the fusion performance, often ignoring the impact of dataset scene bias on model training. This oversight leads the model to learn spurious correlations between specific scenes and fusion weights under conventional likelihood estimation framework, thereby limiting fusion performance. To solve the above problems, this paper first re-examines the image fusion task from the causality perspective, and disentangles the model from the impact of bias by constructing a tailored causal graph to clarify the causalities among the variables in image fusion task. Then, the Back-door Adjustment based Feature Fusion Module (BAFFM) is proposed to eliminate confounder interference and enable the model to learn the true causal effect. Finally, Extensive experiments on three standard datasets prove that the proposed method significantly surpasses state-of-the-art methods in infrared and visible image fusion.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。