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.

通过对 RGB 图像进行数据驱动的光谱重建方法的分析，我们发现当前模型在噪声、压缩和变色上存在限制，然而，通过异色数据增强和光学镜头像差可以提高 RGB 图像对色差信息的编码，从而带来更高性能的光谱成像和重建方法。