Without phase information of the measured field data, the phaseless data inverse scattering
problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full …

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 …

In this letter, a learning-based inversion method with a hybrid input scheme is proposed to
solve quantitative microwave imaging (MI) problems. The high-resolution dielectric targets …

A fast Fourier transform (FFT) twofold subspace-based optimization method (TSOM) is
proposed to solve electromagnetic inverse scattering problems. As mentioned in the original …

In this article, a unified learning-based approach is introduced to solve inverse scattering
problems (ISPs) with mixed boundary conditions (BCs). The scattering behavior of hybrid …

Perfect electric conductors (PECs) are reconstructed integrating the subspace-based
optimization method (SOM) within the iterative multiscaling approach (IMSA). Without a priori …

In this article, we study a 3-D joint inversion algorithm for acoustic and electromagnetic data.
This algorithm is based on the contrast source inversion method, which aims to reconstruct …

We present a formulation of the linear sampling method (LSM) for imaging conducting
targets using a spatially sparse set of receive locations. The technique mitigates the lack of …

To alleviate the extremely intrinsical ill-posedness and nonlinearity of electromagnetic
inverse scattering under high contrast and low signal to noise ratio (SNR), we propose a …

This paper proposes an improved subspace-based optimization method (SOM) by using a
new construction method for the ambiguous part of the induced current. The new current …