Hyperspectral reconstruction (HSR) from RGB images is a fundamentally ill-posed problem due to severe spectral information loss. Existing approaches typically rely on a single RGB image, limiting reconstruction accuracy. In this work, we propose a novel multi-image-to-hyperspectral reconstruction (MI-HSR) framework that leverages a triple-camera smartphone system, where two lenses are equipped with carefully selected spectral filters. Our configuration, grounded in theoretical and empirical analysis, enables richer and more diverse spectral observations than conventional single-camera setups. To support this new paradigm, we introduce Doomer, the first dataset for MI-HSR, comprising aligned images from three smartphone cameras and a hyperspectral reference camera across diverse scenes. We show that the proposed HSR model achieves consistent improvements over existing methods on the newly proposed benchmark. In a nutshell, our setup allows 30% towards more accurately estimated spectra compared to an ordinary RGB camera. Our findings suggest that multi-view spectral filtering with commodity hardware can unlock more accurate and practical hyperspectral imaging solutions.

翻译：从RGB图像进行高光谱重建（HSR）由于严重的光谱信息损失，本质上是一个不适定问题。现有方法通常依赖单一RGB图像，限制了重建精度。本研究提出一种新颖的多图像到高光谱重建（MI-HSR）框架，该框架利用三摄像头智能手机系统，其中两个镜头配备了精心选择的光谱滤波器。基于理论与实证分析的系统配置，能够提供比传统单摄像头设置更丰富、更多样的光谱观测数据。为支持这一新范式，我们推出了首个MI-HSR数据集Doomer，包含来自三个智能手机摄像头和一个高光谱参考摄像头在不同场景下的对齐图像。实验表明，所提出的HSR模型在新提出的基准测试中持续优于现有方法。简而言之，我们的系统配置相比普通RGB相机可将光谱估计精度提升约30%。研究结果表明，利用商用硬件实现多视角光谱滤波能够为高光谱成像提供更精确、更实用的解决方案。