Deep Dichromatic Guided Learning for Illuminant Estimation

Abstract

A new dichromatic illuminant estimation method using a deep neural network is proposed. Previous methods based on the dichromatic reflection model commonly suffer from inaccurate separation of specularity, thus being limited in their use in a real-world. Recent deep neural network-based methods have shown a significant improvement in the estimation of the illuminant color. However, why they succeed or fail is not explainable easily, because most of them estimate the illuminant color at the network output directly. To tackle these problems, the proposed architecture is designed to learn dichromatic planes and their confidences using a deep neural network with novel losses function. The illuminant color is estimated by a weighted least mean square of these planes. The proposed dichromatic guided learning not only achieves compelling results among state-of-the-art color constancy methods in standard real-world benchmark evaluations, but also provides a map to include color and regional contributions for illuminant estimation, which allow for an in-depth analysis of success and failure cases of illuminant estimation.