TY - GEN
T1 - Learning approach to inverse scattering problems with special boundary conditions and inhomogeneous background
AU - Ye, Xiuzhu
AU - Chen, Xudong
N1 - Publisher Copyright:
© 2021 Applied Computational Electromagnetics Society (ACES).
PY - 2021/7/28
Y1 - 2021/7/28
N2 - This article reviews the physics inspired machine learning inverse scattering algorithms developed by the authors for solving mixed boundary condition problem and inhomogeneous background problem, both of which are seldom studied though have found wide applications in practical scenarios such as ground penetrating radar, biomedical imaging and through wall imaging. The difficulty lies in mixed boundary problem is how to choose the parameter representing both PEC and dielectric scatterers as well as how to distinguish them. And the difficulty lies in the inhomogeneous background problem is how to suppress the unwanted artifacts due to the multiple scattering between the scatterers and the background. In this article, a T-matrix based inversion method is proposed to image PEC and dielectric scatterers together, which automatically distinguish the two kinds of scatterers, as the T-matrix naturally contains the information of boundary conditions. The inhomogeneous background problem is solved by generative adversarial neural network (GAN). By introducing the attention scheme into the GAN, the artifacts due to multiple scattering effect between the background and the scatterers are suppressed with a great improvement of resolution. The machine learning part is guided by the physics rather than working as a black box. Therefore, the proposed machine learning approach has a strong generalization ability. Numerical results and experimental results have shown the advantages of the proposed machine learning methods over conventional iterative methods in both image resolution, accuracy and imaging speed.
AB - This article reviews the physics inspired machine learning inverse scattering algorithms developed by the authors for solving mixed boundary condition problem and inhomogeneous background problem, both of which are seldom studied though have found wide applications in practical scenarios such as ground penetrating radar, biomedical imaging and through wall imaging. The difficulty lies in mixed boundary problem is how to choose the parameter representing both PEC and dielectric scatterers as well as how to distinguish them. And the difficulty lies in the inhomogeneous background problem is how to suppress the unwanted artifacts due to the multiple scattering between the scatterers and the background. In this article, a T-matrix based inversion method is proposed to image PEC and dielectric scatterers together, which automatically distinguish the two kinds of scatterers, as the T-matrix naturally contains the information of boundary conditions. The inhomogeneous background problem is solved by generative adversarial neural network (GAN). By introducing the attention scheme into the GAN, the artifacts due to multiple scattering effect between the background and the scatterers are suppressed with a great improvement of resolution. The machine learning part is guided by the physics rather than working as a black box. Therefore, the proposed machine learning approach has a strong generalization ability. Numerical results and experimental results have shown the advantages of the proposed machine learning methods over conventional iterative methods in both image resolution, accuracy and imaging speed.
KW - boundary condition
KW - inhomogeneous background
KW - iteration
KW - physics inspired machine learning
UR - http://www.scopus.com/inward/record.url?scp=85119358083&partnerID=8YFLogxK
U2 - 10.23919/ACES-China52398.2021.9581493
DO - 10.23919/ACES-China52398.2021.9581493
M3 - Conference contribution
AN - SCOPUS:85119358083
T3 - 2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings
BT - 2021 International Applied Computational Electromagnetics Society Symposium, ACES-China 2021, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021
Y2 - 28 July 2021 through 31 July 2021
ER -