Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

In recent years, deep learning (DL) is becoming an increasingly important tool for solving
inverse scattering problems (ISPs). This paper reviews methods, promises, and pitfalls of …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

In this paper, we employ the emerging paradigm of physics-informed neural networks
(PINNs) for the solution of representative inverse scattering problems in photonic …

Imaging through scattering is an important yet challenging problem. Tremendous progress
has been made by exploiting the deterministic input–output “transmission matrix” for a fixed …

Predicting physical response of an artificially structured material is of particular interest for
scientific and engineering applications. Here we use deep learning to predict optical …

Diffraction-limited optical imaging through scattering media has the potential to transform
many applications such as airborne and space-based imaging (through the atmosphere) …

Optical imaging has had a central role in elucidating the underlying biological and
physiological mechanisms in living specimens owing to its high spatial resolution, molecular …

Phase unwrapping is an important but challenging issue in phase measurement. Even with
the research efforts of a few decades, unfortunately, the problem remains not well solved …