# Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
## 5 Conclusions  
This paper proposes an electromagnetic imaging method for uniaxial objects based on a pixel basis. TM and TE waves are respectively emitted toward the uniaxial object, and the scattered fields are received. The initial estimate of the permittivity distribution, obtained via the BPS method, is subsequently input into both the standard GAN and the GAN with SA models to perform real-time reconstruction and comparative analysis. Numerical simulations indicate that, under identical noise conditions and parameter configurations,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Reconstruction Performance**} & **GAN** & **GAN with SA** \\ \hline \multirow{2}{*}{TE} & RMSE & 9.11\% & 7.98\% \\  & SSIM & 88.16\% & 98.18\% \\ \hline \multirow{2}{*}{TM} & RMSE & 7.05\% & 5.81\% \\  & SSIM & 94.38\% & 98.47\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSE and SSIM for permittivity between 2 and 2.5 with 5% noise.  
Figure 14: Reconstructed permittivity ranging from 2 to 2.5 for the GAN without SA input under 5% noise: (**a**) \(\epsilon_{12}\) and (**b**) \(\epsilon_{1x}\), \(\epsilon_{1y}\)  
Figure 15: Reconstructed permittivity ranging from 2 to 2.5 for the GAN with SA input under 5% noise: (**a**) \(\epsilon_{12}\) and (**b**) \(\epsilon_{1x}\), \(\epsilon_{1y}\)  
the GAN with SA yields more accurate reconstructions than the conventional GAN. This discrepancy becomes more pronounced in the TE case, where the vectorial characteristics of the electric field and the complexity of the associated Green's function render the reconstruction task significantly more challenging than in the TM scenario. Furthermore, the permittivity in the cross-sectional plane is represented as a tensor, further increasing the difficulty of reconstruction. Though the BPS method provides a straightforward and computationally efficient initial estimate of the permittivity distribution, with the addition of a discriminator network, integrating an SA mechanism at the end of the generator does enhance learning performance compared with standalone GAN.  
A key limitation of the proposed method arises when the target is embedded in more complex environments, such as half-space media or multilayered dielectric backgrounds. In such cases, the measured scattered fields are significantly influenced by the background stratification, which in turn degrades the accuracy of the initial estimate provided by the BPS. The proposed method holds strong potential for application in real-world scenarios, including biomedical imaging, security screening, and remote sensing, among others. This deterioration in the initial reconstruction may ultimately lead to suboptimal performance of the overall imaging system. In future studies, there remains significant potential for AI to enhance electromagnetic imaging. For instance, implementing lightweight neural network architectures or segmenting scatterers into finer elements to enable high-resolution image reconstruction is expected to become a key area of research in the near term. Although the proposed framework has been validated using synthetic datasets with varying levels of noise and permittivity distributions, it is well-suited for extension to real-world applications. In future work, we intend to test the method on measured scattered field data collected from physical microwave imaging systems. The modular two-stage design, comprising a physics-based initialization and a self-attention-enhanced GAN, offers good adaptability to realistic measurement constraints such as limited view angles, background inhomogeneities, and hardware noise. Such adaptations will be essential for practical deployments in biomedical imaging, nondestructive testing, and security screening scenarios.  
Not applicable  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflict of interest.