microwave imaging

Weakly convex discontinuity adaptive regularization for 3D quantitative microwave tomography

We present an analysis of weakly convex discontinuity adaptive (WCDA) models for regularizing three-dimensional (3D) quantitative microwave imaging. In particular, we are concerned with complex permittivity reconstructions from sparse measurements …

New insights in huber and TV-like regularizers in microwave imaging

In this paper we give new insights into quantitative microwave tomography with robust Huber regularizer and Gauss-Newton optimization. Firstly, we validate this approach for the first time on real electromagnetic measurements. Secondly, we extend the …

Quantitative microwave imaging based on a huber regularization

Reconstruction of inhomogeneous dielectric objects from microwave scattering by means of quantitative microwave tomography is a nonlinear, ill-posed inverse problem. In this paper, we employ the Huber function as a robust regularization approach for …

Weakly convex discontinuity adaptive regularization for microwave imaging

Reconstruction of inhomogeneous dielectric objects from microwave scattering is a nonlinear and ill-posed inverse problem. In this communication, we develop a new class of weakly convex discontinuity adaptive (WCDA) models as a regularization for …

Quantitative microwave tomography from sparse measurements using a robust huber regularizer

In statistical theory, the Huber function yields robust estimations reducing the effect of outliers. In this paper, we employ the Huber function as regularization in a challenging inverse problem: quantitative microwave imaging. Quantitative …

A new discontinuity adaptive method applied to electromagnetic inverse scattering problem

We develop a new regularization method for quantitative microwave tomography, based on a discontinuity adaptive Markov random Field model. The results on 2D data sets are encouraging and indicate potential for use in biomedical imaging.