Optimal Transportation for Example-Guided Color Transfer

In this work, a novel and generic method for example-based color transfer is presented. The color transfer is formulated in two steps: first, an example-based Chromatic Adaptation Transform (CAT) has been designed to obtain an illuminant matching between input and example images. Second, the dominant colors of the input and example images are optimally mapped. The main strength of the method comes from using optimal transportation to map a pair of meaningful color palettes, and regularizing this mapping through thin plate splines. In addition, we show that additional visual or semantic constraints can be seamlessly incorporated to obtain a consistent color mapping. Experiments have shown that the proposed method outperforms state-of-the-art techniques for challenging images. In particular, color mapping artifacts have been objectively assessed by the Structural Similarity (SSIM) measure [26], showing that the proposed approach preserves structures while transferring color. Finally, results on video color transfer show the effectiveness of the method.