# Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
## 1 Introduction
The adversarial loss function enhanced generator learning, thereby improving reconstruction quality [12]. In 2021, [PERSON] introduced a GAN that enhanced image resolution, achieving superior results compared with conventional optimization techniques, particularly in peak signal-to-noise ratio and correlation coefficient [13]. In 2021, [PERSON] proposed a non-iterative Forward Induced Current Learning Method (FICLM) based on GAN Pix2 Pix to solve the electromagnetic scattering problem. Comparisons with other neural networks, such as the U-Net, demonstrated that, thanks to the adversarial framework of Pix2 Pix, FICLM excelled in handling complex scatterers. This method provided a novel deep-learning-based approach for efficiently solving electromagnetic scattering problems [14]. In 2022, [PERSON] proposed a Super-Resolution Generative Adversarial Network (SR-GAN) for quantitative imaging of 2D biaxial anisotropic scatterers. Additionally, the Visual Geometry Group (VGG) loss was introduced to extract high-level features of the target instead of traditional low-level pixel error measurements. Numerical results validated the effectiveness of the proposed method [15]. In 2023, [PERSON] integrated GANs and SA while introducing a weighted loss function, proposing the Learning-Assisted Inversion Method (LAIM) to further enhance the model's generalization capability and stability. Experimental data tested on the FoamDieExt Profile (FDEP) demonstrated that LAIM's combination of SAand the weighted loss function significantly outperformed other methods in image quality and detail restoration, particularly for high-frequency data [16]. In 2024, [PERSON] proposed a deep learning framework based on the Conditional Deep Convolutional Generative Adversarial Network (CDCGAN) to address the EMIS problem. The generator learned the distribution between scattered field data and scatterer contrast, while the discriminator assessed sample authenticity. This method accurately resolved high-contrast scatterer problems, with numerical experiments verifying its high precision and feasibility [17]. This paper introduces an advanced AI technique that integrates SA into a GAN to reconstruct electromagnetic images. Currently, no research explores the application of SA-enhanced GANs in imaging anisotropic objects. Therefore, the Super Resolution Neural Network (SRNN) generator adopts a CNN architecture, which consists of a contracting network on the left and an expanding network on the right. The contracting network incorporates repeated \(3\times 3\) convolution layers, Batch Normalization layer (BN), and Rectified Linear Unit ReLU layer. Additionally, a \(2\times 2\) max pooling layer is introduced in the pooling layer of the contracting network. In the expanding network, \(3\times 3\) up-convolution layer, BN layers, and ReLU layers are added, followed by a \(1\times 1\) convolution layer as the fully connected layer. SA is incorporated at the output of SRNN to enhance the quality of the reconstructed image. The contributions of this paper are summarized as follows:  
1. By separating TE and TM wave incidence, the GAN with SA simultaneously reconstructs the electromagnetic images of anisotropic objects in the x, y, and z directions, making the reconstruction process more challenging. 2. The vector sum of the scattered field for the TE-polarized wave introduces greater complexity compared with that of the TM-polarized wave. 3. The strong nonlinear interaction between the dielectric material and the applied electric field induces significant directionality in the TE waves, leading to less accurate reconstruction in certain directions. 4. In the presence of a discriminator network, integrating an SA mechanism at the end of the generator improves learning performance compared with the absence of SA. In Section 3, the architecture and design of the proposed GAN with SA are detailed. Section 4 provides a comprehensive set of numerical experiments under varying noise conditions.