We present two practical approaches for high fidelity spectral upsampling of previously recorded RGB illumination in the form of an image-based representation such as an RGB light probe. Unlike previous approaches that require multiple measurements with a spectrometer or a reference color chart under a target illumination environment, our method requires no additional information for the spectral upsampling step. Instead, we construct a data-driven basis of spectral distributions for incident illumination from a set of six RGBW LEDs (three narrowband and three broadband) that we employ to represent a given RGB color using a convex combination of the six basis spectra. We propose two different approaches for estimating the weights of the convex combination using – (a) genetic algorithm, and (b) neural networks. We additionally propose a theoretical basis consisting of a set of narrow and broad Gaussians as a generalization of the approach, and also evaluate an alternate LED basis for spectral upsampling. We achieve good qualitative matches of the predicted illumination spectrum using our spectral upsampling approach to ground truth illumination spectrum while achieving near perfect matching of the RGB color of the given illumination in the vast majority of cases. We demonstrate that the spectrally upsampled RGB illumination can be employed for various applications including improved lighting reproduction as well as more accurate spectral rendering.