Microwave imaging (MWI) is a non-invasive technique that can identify unknown scatterer
objects’ features while offering advantages such as low cost and portable devices with respect
to other imaging methods. However, MWI faces challenges in solving the underlying inverse
scattering problem (ISP), which involves recovering target properties from its scattered fields.
Existing methods include linearized and non-linear optimization approaches, but they have
limitations respectively in terms of range of validity and computational complexity (in view of
the possible occurrence of ‘false solutions’). In recent years, learning-based approaches have
emerged as they can allow real-time imaging but usually lack generalizability and a direct
connection to the underlying physics. This paper proposes a physics-informed approach that
combines convolutional neural networks (CNNs) with physics-based calculations. It is based
on a few cascaded operations, making use of the gradient of the relevant cost function, and
successively improving the estimation of the unknown target. The proposed approach is
assessed using simulated as well as experimental Fresnel data. The results show that the
integration of physics with deep learning can contribute to improve reconstruction accuracy,
generalizability, and computational efficiency in MWI.