# Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
## 1 Introduction  
Electromagnetic imaging involves transmitting electromagnetic waves toward a target and reconstructing its size, shape, material properties, and location by analyzing the scattered signals produced from wave-object interactions. Sensing antennas play a crucial role in this process by capturing these scattered signals, which are then used to generate detailed reconstructions of the target. This technology has widespread applications in biomedical imaging, geophysical exploration, remote sensing, and medical diagnostics. Despite its rapid development and widespread applications, electromagnetic imaging faces several challenges, such as poor penetration leading to a low signal-to-noise ratio, nonlinearity, ill-posedness, the curse of dimensionality, computational complexity, and parameter selection issues. Broadly speaking, two primary approaches exist for solving electromagnetic imaging problems: traditional algorithms [1, 2, 3] and Artificial Intelligence (AI)-based methods [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Traditionally, Inverse Scattering Problems (ISPs) transform into optimization problems for solving. However, conventional algorithms require significantcomputational resources and time, limiting their ability to achieve high-resolution reconstructions, while AI-based methods can be broken down into U-Net [4; 5; 6; 7; 8; 9; 10; 11] and GAN-based approaches [12; 13; 14; 15; 16; 17]. In 2020, [PERSON] proposed a Three-Dimensional Electromagnetic Inversion (3D-EMI) method based on Born Approximation (BA) and 3D U-Net. The method improved the initial image using Monte Carlo simulations before training a 3D U-Net. In comparison with the Variational Born Iteration Method (VBIM), this method demonstrated superior performance [4]. In 2021, [PERSON] demonstrated that a multi-frequency U-Net Convolutional Neural Network (CNN) performed well under high-contrast and complex conditions, showing adaptability to different frequency bands [5]. In 2022, [PERSON] introduced a deep learning approach combining U-Net with an attention mechanism to solve ISP. The attention mechanism improved accuracy and efficiency compared with traditional U-Net [6]. In the same year, [PERSON] proposed a deep learning-based down-sampling and up-sampling model to solve the Electromagnetic Inverse Scattering (EMIS) problem. The down-sampling operation extracted relevant feature segments from the measured scattered field and compressed these features, while the up-sampling operation retrieved and expanded these features to reconstruct the dielectric parameters of the unknown scatterer. Numerical results indicated that this method was both effective and feasible for solving the EMIS problems [7]. In 2023, [PERSON] integrated Total Variation (TV) as a regularization term into a Subspace-based Optimization Method (SOM) to preprocess data, and improved U-Net model performance [8]. In 2023, [PERSON] proposed a two-stage deep learning strategy to solve the ISP. In the first stage, a Deep Convolutional Neural Network (DCNN) extracted features from the scattered field data. In the second stage, an improved Attention U-Net refined the reconstructed images. The results indicated that this method outperformed other approaches in terms of image quality and reconstruction accuracy [9]. In 2024, [PERSON] proposed an optimization technique combining U-Net and Cycle-GAN for the EMIS, which significantly refined rough reconstructions generated by U-Net alone [10]. In 2024, [PERSON] proposed a two-stage method to solve the Two-Dimensional Full-Wave (2D Full-Wave) ISP for deriving the dielectric properties of scatterers. First, a BP scheme estimated an initial approximation of the dielectric constant. Then, a U-Net model further optimized the reconstruction quality. Experimental results confirmed that this method was effective and accurate, demonstrating the potential for real-time quantitative imaging applications [11]. [PERSON] applied GANs to solve ISPs with inhomogeneous backgrounds in 2020.