Self‑Attention GAN for Electromagnetic Imaging of Uniaxial Objects
學年 113
學期 2
出版（發表）日期 2025-06-16
作品名稱 Self‑Attention GAN for Electromagnetic Imaging of Uniaxial Objects
作品名稱（其他語言）
著者 Chien-Ching Chiu; Po-Hsiang Chen; Yi-Hsun Chen; Hao Jiang
單位
出版者
著錄名稱、卷期、頁數 Applied Sciences 15(12), p. 6723
摘要 This study introduces a Self-Attention (SA) Generative Adversarial Network (GAN) framework that applies artificial intelligence techniques to microwave sensing for electromagnetic imaging. The approach involves illuminating anisotropic objects using Transverse Magnetic (TM) and Transverse Electric (TE) electromagnetic waves, while sensing antennas collecting the scattered field data. To simplify the training process, a Back Propagation Scheme (BPS) is employed initially to calculate the preliminary permittivity distribution, which is then fed into the GAN with SA for image reconstruction. The proposed GAN with SA offers superior performance and higher resolution compared with GAN, along with enhanced generalization capability. The methodology consists of two main steps. First, TM waves are used to estimate the initial permittivity distribution along the z-direction using BPS. Second, TE waves estimate the x- and y-direction permittivity distribution. The estimated permittivity values are used as inputs to train the GAN with SA. In our study, we add 5% and 20% noise to compare the performance of the GAN with and without SA. Numerical results indicate that the GAN with SA demonstrates higher efficiency and resolution, as well as better generalization capability. Our innovation lies in the successful reconstruction of various uniaxial objects using a generator integrated with a self-attention mechanism, achieving reduced computational time and real-time imaging.
關鍵字 electromagnetic imaging; uniaxial objects; U-Net; deep learning; inverse scattering; generative adversarial network; self-attention
語言 en
ISSN 2076-3417
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127704 )