In coded aperture snapshot spectral imaging (CASSI), the captured measurement entangles spatial and spectral information, posing a severely ill-posed inverse problem for hyperspectral images (HSIs) reconstruction. Moreover, the captured radiance inherently depends on scene illumination, making it difficult to recover the intrinsic spectral reflectance that remains invariant to lighting conditions. To address these challenges, we propose a chromaticity-intensity decomposition framework, which disentangles an HSI into a spatially smooth intensity map and a spectrally variant chromaticity cube. The chromaticity encodes lighting-invariant reflectance, enriched with high-frequency spatial details and local spectral sparsity. Building on this decomposition, we develop CIDNet, a Chromaticity-Intensity Decomposition unfolding network within a dual-camera CASSI system. CIDNet integrates a hybrid spatial-spectral Transformer tailored to reconstruct fine-grained and sparse spectral chromaticity and a degradation-aware, spatially-adaptive noise estimation module that captures anisotropic noise across iterative stages. Extensive experiments on both synthetic and real-world CASSI datasets demonstrate that our method achieves superior performance in both spectral and chromaticity fidelity. Code and models will be publicly available.

翻译：在编码孔径快照光谱成像（CASSI）中，捕获的测量值纠缠了空间与光谱信息，这为高光谱图像（HSI）重建带来了一个严重不适定的逆问题。此外，捕获的辐射亮度本质上依赖于场景光照，使得难以恢复对光照条件保持不变的本征光谱反射率。为应对这些挑战，我们提出了一个色度-强度分解框架，该框架将HSI解耦为一个空间平滑的强度图和一个光谱变化的色度立方体。色度编码了对光照不变的反射率，并富含高频空间细节与局部光谱稀疏性。基于此分解，我们在双相机CASSI系统内开发了CIDNet，即一个色度-强度分解展开网络。CIDNet集成了一个专为重建细粒度且稀疏的光谱色度而设计的混合空间-光谱Transformer，以及一个退化感知、空间自适应的噪声估计模块，该模块能在迭代阶段捕获各向异性噪声。在合成与真实世界CASSI数据集上的大量实验表明，我们的方法在光谱与色度保真度方面均实现了卓越的性能。代码与模型将公开提供。