Abstract

In this paper, a novel deep learning (DL) approach has been proposed to realize high-resolution electromagnetic (EM) inversion imaging. The newly proposed approach is based on the deep convolutional double-module structure (DCDMS), consisting of the pixel-interpolating module and the corresponding quality-improving module. While the pixel-interpolating module roughly increases the 'resolution' of the initial input, the following quality-improving module realizes quantitative EM imaging in high resolution. The input of the proposed DCDMS adopts the mixed input scheme, consisting of the received EM scattered field and the initial reconstruction in much low resolution computed from Gauss-Newton method. The output of the proposed model is the high-resolution contrast (permittivity) 'image' of the target domain. In such manner, the proposed DL approach can make use of much less measurement to realize high-resolution EM inversion imaging accurately and efficiently even for high-contrast scatterers, which can hardly be realized by conventional methods. The training of DCDMS is based on the simple synthetic dataset. Numerical benchmarks are offered to illustrate the excellent performance of DCDMS, which provides a novel thinking for conducting the real-time quantitative EM inversion imaging.

Department(s)

Electrical and Computer Engineering

Publication Status

Early Access

Keywords and Phrases

Convolutional neural network; Deep learning; Electromagnetic inversion 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

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