$\Gamma$-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation

TL;DR

This paper proves the $ ext{Gamma}$-convergence of the Ambrosio-Tortorelli approximation to the Mumford-Shah functional for image segmentation, providing a rigorous foundation for the convergence of minimizers in the piecewise smooth setting.

Contribution

It establishes the first analytical $ ext{Gamma}$-convergence result for the Ambrosio-Tortorelli approximation in the piecewise smooth context, introducing a suitable function space.

Findings

Proves $ ext{Gamma}$-convergence of the approximation scheme.

Ensures convergence of minimizers of the regularized functional.

Provides theoretical validation for numerical methods in image segmentation.

Abstract

Given an image $u_0$, the aim of minimising the Mumford-Shah functional is to find a decomposition of the image domain into sub-domains and a piecewise smooth approximation $u$ of $u_0$ such that $u$ varies smoothly within each sub-domain. Since the Mumford-Shah functional is highly non-smooth, regularizations such as the Ambrosio-Tortorelli approximation can be considered which is one of the most computationally efficient approximations of the Mumford-Shah functional for image segmentation. While very impressive numerical results have been achieved in a large range of applications when minimising the functional, no analytical results are currently available for minimizers of the functional in the piecewise smooth setting, and this is the goal of this work. Our main result is the $\Gamma$-convergence of the Ambrosio-Tortorelli approximation of the Mumford-Shah functional for piecewise smooth approximations. This requires the introduction of an appropriate function space. As a consequence of our $\Gamma$-convergence result, we can infer the convergence of minimizers of the respective functionals.