发表机构
Fraunhofer Heinrich Hertz Institute HHI; Humboldt University Berlin; National Center for Tumordiseases（弗劳恩霍夫海因里希·赫兹研究所; 柏林洪堡大学; 德国肿瘤疾病中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多模态光谱医学成像中跨光谱密集对应数据不足的问题，提出跨光谱调制协议与合成基准，提升模型在光谱不匹配场景下的性能，支撑高光谱成像的空间一致融合。
AI 中文摘要
精确的密集对应是多模态光谱成像系统的基本前提，这类系统融合不同波长范围的数据，用于医学和科学成像中的后续分析。对应的图像点常具有非重叠的光谱灵敏度，导致出现波长依赖的对比度变化、强度反转和外观偏移，而密集真值难以获取，传统基于RGB的训练数据仅能提供有限监督。我们通过在已有的对应基准上引入与传感器无关的跨光谱调制协议，并结合强度输入投影，提出一种合成跨光谱对应基准，用于模拟物理上合理的辐射差异，以此解决数据缺口问题。对采用我们统一跨光谱协议训练的多个现代密集对应骨干网络的评估显示，在严重光谱不匹配的情况下性能大幅提升，同时在标准RGB基准上保持性能。消融实验表明，视角依赖的通道选择和非线性辐射变换提供互补的鲁棒性，说明现有模型的主要局限并非结构匹配能力，而是训练分布与目标图像对光谱特征的不匹配。在异构医学光谱采集系统上的定性评估证明，所提出的训练数据增强协议可作为高光谱成像（HSI）工作流中实现空间一致光谱融合的关键支撑，具有实际应用价值。
英文摘要
Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent analysis in medical and scientific imaging. Corresponding image points are often observed with non-overlapping spectral sensitivities, leading to wavelength-dependent contrast changes, intensity inversions, and appearance shifts for which dense ground truth is difficult to obtain and conventional RGB-based training data provides only limited supervision. We address this data gap by introducing a sensor-agnostic cross-spectral modulation protocol on established correspondence benchmarks with intensity input projection, and by proposing a synthetic cross-spectral correspondence benchmark simulating physically plausible radiometric differences. Evaluation on several modern dense correspondence backbones trained with our unified cross-spectral protocol showed substantial improvements under severe spectral mismatch while maintaining performance on standard RGB benchmarks. Ablation experiments show that view-dependent channel selection and nonlinear radiometric transformations provide complementary robustness, indicating that the primary limitation of existing models is not their structural matching capacity but the mismatch between training distribution and spectral characteristics of the target image pair. Qualitative evaluations on heterogeneous medical spectral acquisition systems demonstrate the practical relevance of the proposed training data augmentation protocol as an enabler for spatially coherent spectral fusion in HSI workflows.
CommentsAccepted at 2nd Data Curation & Augmentation in Medical Imaging Workshop at ECCV 2026
