Adaptive Illuminant Estimation Using Neural Networks
In this article we present results that show that a multi-layer neural network can be trained to estimate the chromaticity of the scene illumination. The network is trained with a set of artificially generated scenes and the corresponding illuminants that were used for generating the scenes. In the test phase, the network estimates the illuminant under which a given scene was taken. Tests with artificially generated as well as natural scenes show that the neural network outperforms many current color-constancy algorithms and that it is more stable and reliable for scenes that contain a small number of colors.