发表机构
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究非朗伯场景下高光谱内在分解问题，重新审视双色反射模型，提出统一反演范式及双尺度分解方案，建立CITE数据集和PISG，通过实验证明方法在多样场景下的有效性和鲁棒性。
AI 中文摘要
高光谱内在分解（HID）旨在解开高光谱图像（HSIs）中与材料相关的光谱特性和光度效应，对理解真实世界成像过程至关重要且有益于多种下游应用。多数现有HID研究基于朗伯或近朗伯假设。少数非朗伯研究依赖简化镜面假设，不足以处理多样真实镜面反射，且通常需辅助输入或仅恢复部分耦合分量，阻碍完整和盲分解。本文重新审视双色反射模型（DRM），开发统一反演范式，将四个耦合反射率和光度分量的恢复重新表述为两个光谱-空间目标变量的估计。在此基础上，提出双尺度分解方案处理具有不同空间特征的非朗伯效应。在全局尺度，光度不变描述符作为边缘先验用于高保真内在边界保留；在局部尺度，镜面反射引导注意力强调受裁剪失真影响的镜面反射主导区域进行细化。为促进未来研究，建立首个非朗伯物体的公共真实世界HID数据集CITE，并开发用于可控数据合成的物理忠实内在集生成器（PISG）。在CITE和其他HSIs上的大量消融研究和实验证明了该方法的有效性和跨多样场景的鲁棒性。
英文摘要
Hyperspectral intrinsic decomposition (HID) aims to disentangle material-related spectral properties and photometric effects in hyperspectral images (HSIs), which is essential for understanding real-world imaging processes and benefits a variety of downstream applications. Most existing HID studies have been developed under Lambertian or near-Lambertian assumptions. The few prior non-Lambertian efforts rely on simplified specular assumptions insufficient to handle diverse real-world specularity, and typically require auxiliary inputs or recover only a subset of the coupled reflectance and photometric components, hindering complete and blind decomposition. In this paper, we revisit the dichromatic reflection model (DRM) and develop a unified inversion paradigm that reformulates the recovery of four coupled reflectance and photometric components as the estimation of two spectral--spatial target variables. Building on this reformulation, we propose a dual-scale decomposition scheme to handle non-Lambertian effects with distinct spatial characteristics. At the global scale, photometrically invariant descriptors serve as edge priors for high-fidelity intrinsic boundary preservation; at the local scale, specularity-guided attention directs refinement with emphasis on specularity-dominated regions, including those affected by clipping distortion. To facilitate future research, we establish CITE, the first public real-world HID dataset for non-Lambertian objects, and develop a Physically-faithful Intrinsic Set Generator (PISG) for controllable data synthesis. Extensive ablation studies and experiments on the CITE and additional HSIs demonstrate the effectiveness of our method and its robustness across diverse scenes.
