Some objects such as mineral substances may be both dielectric and magnetic and their
high-resolution inner imaging is very desirable when detecting and analyzing their …

Conventional deep learning-based image reconstruction methods require a large amount of
training data, which can be hard to obtain in practice. Untrained deep learning methods …

Recently, many successes have been witnessed in the field of inverse scattering problems
(ISPs) with deep learning schemes (DLSs). However, most of the studies focus on the spatial …

Objective. Quantitative phase retrieval (QPR) in propagation-based x-ray phase contrast
imaging of heterogeneous and structurally complicated objects is challenging under …

Frequency-diverse radar imaging is an emerging field that combines computational imaging
with frequency-diverse techniques to interrogate the high-quality images of objects. Despite …

The millimeter-wave frequency-diverse imaging regime has recently received considerable
attention in both the security screening and synthetic aperture radar imaging literature …

We consider in this work an inverse acoustic scattering problem when only phaseless data
is available. The inverse problem is highly nonlinear and ill-posed due to the lack of the …

With the goal of improving machine learning approaches in inverse scattering, we overview
an experimental data set collected with a near-field microwave imaging system. Machine …

The ability to sculpt complex reference waves and probe diverse radiation field patterns
have facilitated the rise of metasurface antennas, while there is still a compromise between …

A deep learning method based on coupled scattering has been investigated in this work.
The aim is to obtain the composite scattering characteristics by using independent …