Improving RGB illuminant estimation exploiting spectral average radiance.

We introduce a method that enhances RGB color constancy accuracy by combining neural network and -means clustering techniques. Our approach stands out from previous works because we combine multispectral and color information together to estimate illuminants. Furthermore, we investigate the combination of the illuminant estimation in the RGB color and in the spectral domains, as a strategy to provide a refined estimation in the RGB color domain. Our investigation can be divided into three main points: (1) identify the spatial resolution for sampling the input image in terms of RGB color and spectral information that brings the highest performance; (2) determine whether it is more effective to predict the illuminant in the spectral or in the RGB color domain, and finally, (3) assuming that the illuminant is in fact predicted in the spectral domain, investigate if it is better to have a loss function defined in the RGB color or spectral domain. Experimental results are carried out on NUS: a standard dataset of multispectral radiance images with an annotated spectral global illuminant. Among the several considered options, the best results are obtained with a model trained to predict the illuminant in the spectral domain using an RGB color loss function. In terms of comparison with the state of the art, this solution improves the recovery angular error metric by 66% compared to the best tested spectral method, and by 41% compared to the best tested RGB method.