GAN-driven Electromagnetic Imaging of 2-D Dielectric Scatterers
Ehtasham Naseer Ali Imran Sandhu Muhammad Adnan Siddique Waqas W. Ahmed Mohamed Farhat Ying Wu
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.