GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers

arXiv:2402.10831v1 Announce Type: cross
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) …

abstract adversarial arxiv autoencoder cs.ce cs.lg deep learning eess.iv eess.sp electric fields gan generative generative adversarial networks imaging networks objects paper type