Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects
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 (...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6723 |
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| author | Chien-Ching Chiu Po-Hsiang Chen Yi-Hsun Chen Hao Jiang |
| author_facet | Chien-Ching Chiu Po-Hsiang Chen Yi-Hsun Chen Hao Jiang |
| author_sort | Chien-Ching Chiu |
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| description | 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. |
| format | Article |
| id | doaj-art-1d1d0241449c408c90ffef84b5acb3fb |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1d1d0241449c408c90ffef84b5acb3fb2025-08-20T03:32:27ZengMDPI AGApplied Sciences2076-34172025-06-011512672310.3390/app15126723Self-Attention GAN for Electromagnetic Imaging of Uniaxial ObjectsChien-Ching Chiu0Po-Hsiang Chen1Yi-Hsun Chen2Hao Jiang3Department of Electrical and Computer Engineering, Tamkang University, Tamsui 251301, TaiwanDepartment of Electrical and Computer Engineering, Tamkang University, Tamsui 251301, TaiwanDepartment of Electrical and Computer Engineering, Tamkang University, Tamsui 251301, TaiwanSchool of Engineering, San Francisco State University, San Francisco, CA 94117-1080, USAThis 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.https://www.mdpi.com/2076-3417/15/12/6723electromagnetic imaginguniaxial objectsU-Netdeep learninginverse scatteringgenerative adversarial network |
| spellingShingle | Chien-Ching Chiu Po-Hsiang Chen Yi-Hsun Chen Hao Jiang Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects Applied Sciences electromagnetic imaging uniaxial objects U-Net deep learning inverse scattering generative adversarial network |
| title | Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects |
| title_full | Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects |
| title_fullStr | Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects |
| title_full_unstemmed | Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects |
| title_short | Self-Attention GAN for Electromagnetic Imaging of Uniaxial Objects |
| title_sort | self attention gan for electromagnetic imaging of uniaxial objects |
| topic | electromagnetic imaging uniaxial objects U-Net deep learning inverse scattering generative adversarial network |
| url | https://www.mdpi.com/2076-3417/15/12/6723 |
| work_keys_str_mv | AT chienchingchiu selfattentionganforelectromagneticimagingofuniaxialobjects AT pohsiangchen selfattentionganforelectromagneticimagingofuniaxialobjects AT yihsunchen selfattentionganforelectromagneticimagingofuniaxialobjects AT haojiang selfattentionganforelectromagneticimagingofuniaxialobjects |