A transformer-embedded learning method for solving full-wave inverse scattering problems

Bo Sun; Fan Wu; Cong Zhang; Ruiqi Su; Wenhao Fan; Yuanan Liu

doi:10.1117/12.3045770

13 December 2024 A transformer-embedded learning method for solving full-wave inverse scattering problems

Bo Sun, Fan Wu, Cong Zhang, Ruiqi Su, Wenhao Fan, Yuanan Liu

Proceedings Volume 13496, AOPC 2024: Optical Sensing, Imaging Technology, and Applications; 134961H (2024) https://doi.org/10.1117/12.3045770
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China

Abstract

Electromagnetic inverse scattering problems (ISPs) have been constrained by challenges such as nonlinearity and ill-posedness. This paper introduces a learning-based electromagnetic inversion approach without iterative computation dealing with these challenges. A residual block-constructed U-shape network called TransISP is developed to address the dense pixel-level microwave imaging task. Transformer blocks are embedded as the bottleneck of the TransISP to extract the latent features of the high-contrast target. Numerical experimental results demonstrate that TransISP can effectively learn the knowledge mapping the radiative part to the total contrast and outperforms the method without transformers in the quality of inversions.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bo Sun, Fan Wu, Cong Zhang, Ruiqi Su, Wenhao Fan, and Yuanan Liu "A transformer-embedded learning method for solving full-wave inverse scattering problems", Proc. SPIE 13496, AOPC 2024: Optical Sensing, Imaging Technology, and Applications, 134961H (13 December 2024); https://doi.org/10.1117/12.3045770

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