Parametric Level Set Methods for Inverse Problems

TL;DR

This paper introduces a parametric level set method using radial basis functions for inverse problems, reducing problem dimensionality and improving computational efficiency in obstacle reconstruction tasks.

Contribution

The paper proposes a novel parametric level set approach with radial basis functions, simplifying inverse problem reconstruction and enabling efficient Newton-based optimization.

Findings

Effective obstacle reconstruction in electrical resistance tomography

Successful application to X-ray computed tomography

Improved computational efficiency with narrow-banding technique

Abstract

In this paper, a parametric level set method for reconstruction of obstacles in general inverse problems is considered. General evolution equations for the reconstruction of unknown obstacles are derived in terms of the underlying level set parameters. We show that using the appropriate form of parameterizing the level set function results a significantly lower dimensional problem, which bypasses many difficulties with traditional level set methods, such as regularization, re-initialization and use of signed distance function. Moreover, we show that from a computational point of view, low order representation of the problem paves the path for easier use of Newton and quasi-Newton methods. Specifically for the purposes of this paper, we parameterize the level set function in terms of adaptive compactly supported radial basis functions, which used in the proposed manner provides flexibility in presenting a larger class of shapes with fewer terms. Also they provide a "narrow-banding" advantage which can further reduce the number of active unknowns at each step of the evolution. The performance of the proposed approach is examined in three examples of inverse problems, i.e., electrical resistance tomography, X-ray computed tomography and diffuse optical tomography.