Robust Image-to-Image Color Transfer Using Optimal Inlier Maximization

Magnus Oskarsson; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 786-795

In this paper we target the color transfer estimation problem, when we have pixel-to-pixel correspondences. We present a feature-based method, that robustly fits color transforms to data containing gross outliers. Our solution is based on an optimal inlier maximization algorithm that maximizes the number of inliers in polynomial time. We introduce a simple feature detector and descriptor based on the structure tensor that gives the means for reliable matching of the color distributions in two images. Using combinatorial methods from optimization theory and a number of new minimal solvers, we can enumerate all possible stationary points to the inlier maximization problem. In order for our method to be tractable we use a decoupling of the intensity and color direction for a given RGB-vector. This enables the intensity transformation and the color direction transformation to be handled separately. Our method gives results comparable to state-of-the-art methods in the presence of little outliers, and large improvement for moderate or large amounts of outliers in the data. The proposed method has been tested in a number of imaging applications.