首页|VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems

VBIM-Net: Variational Born Iterative Network for Inverse Scattering Problems

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  • NETL
  • NSTL
  • IEEE
Recently, studies have shown the potential of integrating field-type iterative methods with deep learning (DL) techniques in solving inverse scattering problems (ISPs). In this article, we propose a novel variational Born iterative network (VBIM-Net), to solve the full-wave ISPs with significantly improved structural rationality and inversion quality. The proposed VBIM-Net emulates the alternating updates of the total electric field and the contrast in the variational Born iterative method (VBIM) by multiple layers of subnetworks. We embed the analytical calculation of the contrast variation into each subnetwork, converting the scattered field residual into an approximate contrast variation and then enhancing it by a U-Net, thus avoiding the requirement of matched measurement dimension and grid resolution as in existing approaches. The total field and contrast of each layer’s output are supervised in the loss function of VBIM-Net, imposing soft physical constraints on the variables in the subnetworks, which benefits the model’s performance. In addition, we design a training scheme with extra noise to enhance the model’s stability. Extensive numerical results on synthetic and experimental data both verify the inversion quality, generalization ability, and robustness of the proposed VBIM-Net. This work may provide some new inspiration for the design of efficient field-type DL schemes.

Iterative methodsTrainingImage reconstructionTranslationRobustnessGreen's function methodsPermittivityInverse problemsGenerative adversarial networksComputational modeling

Ziqing Xing、Zhaoyang Zhang、Zirui Chen、Yusong Wang、Haoran Ma、Zhun Wei

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College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China|Institute of Fundamental and Transdisciplinary Research, Zhejiang University, Hangzhou, China|Zhejiang Provincial Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing, Hangzhou, China

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China|Zhejiang Provincial Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing, Hangzhou, China

School of Mathematical Sciences, Zhejiang University, Hangzhou, China

College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

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2025

10.1109/TGRS.2025.3532363

IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing

IEEE transactions on geoscience and remote sensing: Institute of Electrical and Electronics Engineers transactions on geoscience and remote sensing

ISSN：
年,卷(期)：2025.63(1)
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