Approximate Cross Channel Color Mapping from Sparse Color Correspondences

We propose a color mapping method that compensates color differences between images having a common semantic content such as multiple views of a scene taken from different viewpoints. A so-called color mapping model is usually estimated from color correspondences selected from those images. In this work, we introduce a color mapping that model color change in two steps: first, nonlinear, channel-wise mapping, second, linear, cross-channel mapping. Additionally, unlike many state of the art methods, we estimate the model from sparse matches and do not require dense geometric correspondences. We show that well known cross-channel color change can be estimated from sparse color correspondence. Quantitative and visual benchmark tests show good performance compared to recent methods in literature.