Recently, tremendous progress has been achieved in applying deep learning schemes
(DLSs) to solve inverse scattering problems (ISPs), where state-of-the-art performance has …

In this work, we present a deep-learning-based low-frequency (LF) data prediction scheme
to solve the highly nonlinear inverse scattering problem (ISP) with strong scatterers. The …

A family of new integral equations (NIE) is proposed in this paper, which are transformed
from the original Lippmann-Schwinger integral equation. It can be shown that the NIE can …

The linear sampling method (LSM) is a simple and effective approach to image the shape of
unknown targets via the solution of a linear inverse problem. In this paper, we show that the …

Microwave tomography deserves attention in biomedical imaging, owing to its potential
capability of providing a morphological and functional assessment of the inspected tissues …

An optimization-based confocal algorithm for microwave imaging aimed at medical
applications is presented. Due to complexity of human body tissues, microwave signals that …

In microwave imaging, the linearity of the relationship between the incident and the
scattered field offers the possibility of a posteriori recombining the performed scattering …

In this article, to bridge the gap between the traditional model-based methods and data-
driven deep learning schemes, we propose a physical model-inspired deep unrolling …

Concrete is one of the most commonly used construction materials in civil engineering.
Being able to accurately predict concrete components based on concrete strength, slump …

The iterative Born approximation (IBA) is a well-known method for describing waves
scattered by semitransparent objects. In this letter, we present a novel nonlinear inverse …