Abstract

In this paper, we introduce an innovative deep learning (DL) methodology designed for real-time quantitative microwave imaging (MWI). Our approach is centered around the utilization of a deep convolutional asymmetric encoder-decoder structure (DCAEDS), which requires only a single-frequency far-field measurement of the electromagnetic (EM) scattered field as input and subsequently predicts the contrasts (permittivities) of the target materials. During the offline training process, we incorporate an EM forward solver specifically crafted to compute the EM scattered field generated by the predicted target contrasts (permittivities) produced by the DCAEDS. The DCAEDS is seamlessly integrated with this EM forward solver to optimize the loss function. This loss function comprises two fundamental components: (1) Data-induced loss, directly quantifying the dissimilarity between the predictions of our proposed DCAEDS and the actual labels for the target contrasts (permittivities); (2) Physics-induced loss, evaluating the distinctions between the measured EM scattered field and the computed EM scattered field derived from the predicted target contrasts (permittivities) generated by the DCAEDS. Our DL approach excels in delivering precise results, even for high-contrast targets, overcoming limitations associated with conventional methods, such as computational cost and ill-conditioning. Numerical benchmarks using dielectric targets underscore the practicality and effectiveness of our DL-based approach.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Convolutional neural network; deep learning; high-contrast; microwave imaging

International Standard Serial Number (ISSN)

2333-9403; 2573-0436

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2026

Download

Full Text Link

Share

 
COinS