We demonstrate that a deep neural network can achieve near-perfect colour correction for the RGB signals from the sensors in a camera under a wide range of daylight illumination spectra. The network employs a fourth input signal representing the correlated colour temperature of the illumination. The network was trained entirely on synthetic spectra and applied to a set of RGB images derived from a hyperspectral image dataset under a range of daylight illumination with CCT from 2500K to 12500K. It produced an invariant output image as XYZ referenced to D65, with a mean colour error of approximately 1.0 ΔE*ab.
Lindsay MacDonald, Katarina Mayer, "Camera Colour Correction using Neural Networks" in Proc. IS&T London Imaging Meeting 2021: Imaging for Deep Learning, 2021, pp 54 - 57, https://doi.org/10.2352/issn.2694-118X.2021.LIM-54