An Enhanced U-Net Scheme with Self-Attention Mechanism for Inverse Scattering Problems
- Resource Type
- Conference
- Authors
- Du, Changlin; Pan, Jin; Yang, Deqiang; Chen, Yongpin
- Source
- 2023 International Applied Computational Electromagnetics Society Symposium (ACES-China) Applied Computational Electromagnetics Society Symposium (ACES-China), 2023 International. :1-3 Aug, 2023
- Subject
- Engineering Profession
Fields, Waves and Electromagnetics
Training
Deep learning
Inverse problems
Neural networks
Scattering
Computer architecture
Network architecture
Inverse scattering problems
deep learning
self-attention mechanism
- Language
The nonlinearity and ill-posedness of the inverse scattering problems make them difficult to solve. Multiple scattering effects are the main cause of nonlinearity. In this paper, a deep learning method with self-attention mechanism is proposed to address the difficulties caused by multiple scattering effects. The main block of the proposed network architecture combines self-attention layers and convolutional layers, enhancing the global perception ability of the network. Numerical results show that the proposed method achieves higher accuracy in solving the inverse scattering problem compared to architectures relying solely on convolutional neural networks.