Deep Learning Based High-Resolution Electromagnetic Inversion Imaging Using Deep Convolutional Double-Module Structure

doi:10.1109/tci.2026.3707042

Deep Learning Based High-Resolution Electromagnetic Inversion Imaging Using Deep Convolutional Double-Module Structure

2026-06-24

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DOI：10.1109/tci.2026.3707042

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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.

Keywords:

Convolutional neural network

deep learning

electromagnetic inversion imaging

Deep Learning Based High-Resolution Electromagnetic Inversion Imaging Using Deep Convolutional Double-Module Structure

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