GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

• URL: http://arxiv.org/abs/2402.10831v1
• Date: Fri, 16 Feb 2024 17:03:08 GMT
• Title: GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers
• Authors: Ehtasham Naseer, Ali Imran Sandhu, Muhammad Adnan Siddique, Waqas W. Ahmed, Mohamed Farhat, and Ying Wu
• Abstract summary: 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. A cohesive inverse neural network (INN) framework is set up comprising a sequence of appropriately designed dense layers. 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 $0.90$.
• Score: 4.510838705378781
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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