A generalized model for optimal transport of images including dissipation and density modulation

TL;DR

This paper introduces a comprehensive model combining optimal transport and metamorphosis for images, capable of handling contrast variations, dissipation, and sources, with a robust discretization and applications to real images.

Contribution

It develops a generalized variational framework for image transport that includes dissipation and density modulation, extending previous models with a new discretization and implementation.

Findings

Proven existence of geodesic paths in measure space.

Effective variational time discretization for geodesic computation.

Successful application to real images and comparison with existing methods.

Abstract

In this paper the optimal transport and the metamorphosis perspectives are combined. For a pair of given input images geodesic paths in the space of images are defined as minimizers of a resulting path energy. To this end, the underlying Riemannian metric measures the rate of transport cost and the rate of viscous dissipation. Furthermore, the model is capable to deal with strongly varying image contrast and explicitly allows for sources and sinks in the transport equations which are incorporated in the metric related to the metamorphosis approach by Trouv\'e and Younes. In the non-viscous case with source term existence of geodesic paths is proven in the space of measures. The proposed model is explored on the range from merely optimal transport to strongly dissipative dynamics. For this model a robust and effective variational time discretization of geodesic paths is proposed. This requires to minimize a discrete path energy consisting of a sum of consecutive image matching functionals. These functionals are defined on corresponding pairs of intensity functions and on associated pairwise matching deformations. Existence of time discrete geodesics is demonstrated. Furthermore, a finite element implementation is proposed and applied to instructive test cases and to real images. In the non-viscous case this is compared to the algorithm proposed by Benamou and Brenier including a discretization of the source term. Finally, the model is generalized to define discrete weighted barycentres with applications to textures and objects.