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Hyperspectral imaging empowers machine vision systems with the distinctcapability of identifying materials through recording their spectralsignatures. Recent efforts in data-driven spectral reconstruction aim atextracting spectral information from RGB images captured by cost-effective RGBcameras, 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 andoverfitting, as well as fundamental limitations with respect to the nature ofthe information encoded in the RGB images, and the dependency of thisinformation 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 modelingunderrepresented spectral content, existing datasets and the models trained onthem are limited in their ability to cope with challenging metameric colors. Tomitigate this issue, we propose to exploit the combination of metameric dataaugmentation and optical lens aberrations to improve the encoding of themetameric information into the RGB image, which paves the road towards higherperforming spectral imaging and reconstruction approaches.

Limitations of Data-Driven Spectral Reconstruction -- Optics-Aware Analysis and Mitigation