CVDMOCJul 21, 2013

Regularized Discrete Optimal Transport

arXiv:1307.5551v177 citations
Originality Incremental advance
AI Analysis

This work addresses color processing challenges in image editing, offering incremental improvements for tasks like color transfer and normalization.

The paper tackles the problem of color image manipulation by introducing a generalization of discrete optimal transport that relaxes mass conservation and adds regularization, resulting in a robust color transfer map that reduces artifacts and handles mass variations across images. It also extends this to compute barycenters for color normalization across multiple images.

This article introduces a generalization of the discrete optimal transport, with applications to color image manipulations. This new formulation includes a relaxation of the mass conservation constraint and a regularization term. These two features are crucial for image processing tasks, which necessitate to take into account families of multimodal histograms, with large mass variation across modes. The corresponding relaxed and regularized transportation problem is the solution of a convex optimization problem. Depending on the regularization used, this minimization can be solved using standard linear programming methods or first order proximal splitting schemes. The resulting transportation plan can be used as a color transfer map, which is robust to mass variation across images color palettes. Furthermore, the regularization of the transport plan helps to remove colorization artifacts due to noise amplification. We also extend this framework to the computation of barycenters of distributions. The barycenter is the solution of an optimization problem, which is separately convex with respect to the barycenter and the transportation plans, but not jointly convex. A block coordinate descent scheme converges to a stationary point of the energy. We show that the resulting algorithm can be used for color normalization across several images. The relaxed and regularized barycenter defines a common color palette for those images. Applying color transfer toward this average palette performs a color normalization of the input images.

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