Hyperspectral imaging empowers computer vision systems with the distinct capability of identifying materials through recording their spectral signatures. Recent efforts in data-driven spectral reconstruction aim at extracting spectral information from RGB images captured by cost-effective RGB cameras, instead of dedicated hardware. In this paper we systematically analyze the performance of such methods, evaluating both the practical limitations with respect to current datasets and overfitting, as well as fundamental limits with respect to the nature of the information encoded in the RGB images, and the dependency of this information on the optical system of the camera. We find that the current models are not robust under slight variations, e.g., in noise level or compression of the RGB file. Both the methods and the datasets are also limited in their ability to cope with metameric colors. This issue can in part be overcome with metameric data augmentation. Moreover, optical lens aberrations can help to improve the encoding of the metameric information into the RGB image, which paves the road towards higher performing spectral imaging and reconstruction approaches.

翻译：高光谱成像通过记录材料的光谱特征，使计算机视觉系统具备识别材料的独特能力。近年来，数据驱动的光谱重建研究旨在利用成本效益较高的RGB相机拍摄的图像提取光谱信息，而非依赖专用硬件。本文系统分析了此类方法的性能，评估了当前数据集与过拟合方面的实际局限性，以及RGB图像编码信息本质所决定的基本限度，并探讨了该信息对相机光学系统的依赖性。研究发现，当前模型在噪声水平或RGB文件压缩等轻微变化下缺乏鲁棒性。现有方法与数据集在应对同色异谱颜色时也存在能力局限，而通过同色异谱数据增强可部分克服此问题。此外，光学镜头像差有助于改善同色异谱信息在RGB图像中的编码，为更高性能的光谱成像与重建方法研究铺平了道路。