AI 中文总结
针对RGB-T融合中的空间误配准与跨模态差异问题，提出SAGE统一框架，集成频率均衡、层级对齐与子带融合，通过源锚定引导实现弱配准图像的有效对齐与融合。
AI 中文摘要
空间误配准和跨模态差异常导致RGB-T融合中出现重影、结构模糊和内容失衡。现有方法通常将外观适应、几何对齐和信息融合解耦，限制了跨阶段的依赖传播。我们提出基于频率均衡的源锚定引导用于层级RGB-T对齐与融合（SAGE），这是一个统一框架，集成了频率均衡、层级对齐和子带融合。SAGE采用可逆联合编码和源特定低频调制来导出结构和增益引导，同时保留源信息。层级频率协同对齐从低频近似中估计全局仿射几何，并将几何和上下文线索传递到高频相关性推理，以实现可靠性感知的残差细化。引导的子带融合在传播的源和对齐引导下联合聚合对齐的频率系数，协调互补的低频和高频信息，并通过逆小波变换重建融合图像。在具有真实世界和合成误配准的RGB-T数据集上进行的大量实验表明，在对齐和融合方面持续表现出竞争性性能，验证了源锚定引导对弱配准RGB-T图像的有效性。
英文摘要
Spatial misregistration and cross-modal discrepancies often cause ghosting, structural blurring, and content imbalance in RGB-T fusion. Existing methods typically decouple appearance adaptation, geometric alignment, and information fusion, limiting dependency propagation across stages. We propose Source-Anchored Guidance via Frequency Equalization for Hierarchical RGB-T Alignment and Fusion (SAGE), a unified framework integrating frequency equalization, hierarchical alignment, and subband fusion. SAGE employs invertible joint encoding and source-specific low-frequency modulation to derive structural and gain guidance while preserving source information. Hierarchical frequency collaborative alignment estimates global affine geometry from low-frequency approximations and transfers geometric and contextual cues to high-frequency correlation reasoning for reliability-aware residual refinement. Guided subband fusion jointly aggregates the aligned frequency coefficients under propagated source and alignment guidance, coordinates complementary low- and high-frequency information, and reconstructs the fused image through the inverse wavelet transform. Extensive experiments on RGB-T datasets with real-world and synthetic misalignments demonstrate consistently competitive performance in alignment and fusion, validating the effectiveness of source-anchored guidance for weakly registered RGB-T images.
