On a novel inverse scattering scheme using resonant modes with enhanced imaging resolution

TL;DR

This paper introduces a new inverse scattering method that leverages interior resonant modes to improve shape reconstruction and imaging resolution of obstacles, especially enhancing details of concave regions.

Contribution

The study presents a novel approach that uses interior resonant modes for obstacle reconstruction, including a numerical procedure for eigenvalue determination and optimization for eigenfunctions.

Findings

Effective shape reconstruction using resonant modes

Enhanced imaging resolution for concave obstacle parts

Numerical validation in 2D and 3D scenarios

Abstract

We develop a novel wave imaging scheme for reconstructing the shape of an inhomogeneous scatterer and we consider the inverse acoustic obstacle scattering problem as a prototype model for our study. There exists a wealth of reconstruction methods for the inverse obstacle scattering problem and many of them intentionally avoid the interior resonant modes. Indeed, the occurrence of the interior resonance may cause the failure of the corresponding reconstruction. However, based on the observation that the interior resonant modes actually carry the geometrical information of the underlying obstacle, we propose an inverse scattering scheme of using those resonant modes for the reconstruction. To that end, we first develop a numerical procedure in determining the interior eigenvalues associated with an unknown obstacle from its far-field data based on the validity of the factorization method. Then we propose two efficient optimization methods in further determining the corresponding eigenfunctions. Using the afore-determined interior resonant modes, we show that the shape of the underlying obstacle can be effectively recovered. Moreover, the reconstruction yields enhanced imaging resolution, especially for the concave part of the obstacle. We provide rigorous theoretical justifications for the proposed method. Numerical examples in 2D and 3D verify the theoretically predicted effectiveness and efficiency of the method.