  Hyperspectral imaging empowers machine 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 limitations 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. Without modeling
underrepresented spectral content, existing datasets and the models trained on
them are limited in their ability to cope with challenging metameric colors. To
mitigate this issue, we propose to exploit the combination of metameric data
augmentation and optical lens aberrations to improve the encoding of the
metameric information into the RGB image, which paves the road towards higher
performing spectral imaging and reconstruction approaches.
