# GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

arXiv: https://arxiv.org/abs/2402.10831 · HTML: https://mlsanity.com/paper/2402.10831/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [DeepNIS: Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering](https://mlsanity.com/paper/1810.03990/) (2018)
- [Probability-Density-Based Deep Learning Paradigm for the Fuzzy Design of Functional Metastructures](https://mlsanity.com/paper/2011.05516/) (2020)
- [ANN-assisted CoSaMP Algorithm for Linear Electromagnetic Imaging of Spatially Sparse Domains](https://mlsanity.com/paper/1911.06514/) (2019)
- [Machine learning for knowledge acquisition and accelerated inverse-design for non-Hermitian systems](https://mlsanity.com/paper/2204.13376/) (2022)
- [A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data](https://mlsanity.com/paper/2207.05433/) (2022)
- [An Accelerated Nonlinear Contrast Source Inversion Scheme For Sparse Electromagnetic Imaging](https://mlsanity.com/paper/1911.06020/) (2019)
- [A Trained Regularization Approach Based on Born Iterative Method for Electromagnetic Imaging](https://mlsanity.com/paper/2112.13367/) (2021)

## Abstract (arXiv)

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and efficiently reconstruct randomly-shaped two-dimensional dielectric objects from amplitudes of multi-frequency scattered electric fields. An adversarial autoencoder (AAE) is trained to learn to generate the scatterer's geometry from a lower-dimensional latent representation constrained to adhere to the Gaussian distribution. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers, the already-trained generator as well as a separately trained forward neural network. The images reconstructed at the output of the inverse network are validated through comparison with outputs from the forward neural network, addressing the non-uniqueness challenge inherent to electromagnetic (EM) imaging problems. The trained INN demonstrates an enhanced robustness, evidenced by a mean binary cross-entropy (BCE) loss of 0.13 and a structure similarity index (SSI) of 0.90. The study not only demonstrates a significant reduction in computational load, but also marks a substantial improvement over traditional objective-function-based methods. It contributes both to the fields of machine learning and EM imaging by offering a real-time quantitative imaging approach. The results obtained with the simulated data, for both training and testing, yield promising results and may open new avenues for radio-frequency inverse imaging.