用深度学习融合物理规律，高效解决多频电磁反散射成像问题。
Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems
- 基于物理引导的多任务学习，自适应分配各频段数据权重。
- 无需对比和总场数据，合成与实测数据均显示精度提升。
- 抗噪强、泛化好，适合复杂电磁环境下的成像应用。
本文提出一种基于深度学习的成像方法，用于解决多频电磁反散射问题（ISP）。通过结合深度学习与电磁物理规律，构建了多频神经玻恩迭代法（NeuralBIM），其设计受单频NeuralBIM启发。该方法融合多任务学习与NeuralBIM高效的迭代反演流程，形成稳健的多频玻恩迭代反演模型。训练过程中，采用同方差不确定性引导的多任务学习策略，自适应分配各频率数据权重；同时，利用受物理规律约束的无监督学习方式训练模型，避免对对比场和总场数据的依赖。在合成与实验数据上的验证表明，该方法在反散射问题求解中显著提升了精度与计算效率，并展现出强泛化能力与抗噪性能。多频NeuralBIM为多频电磁数据反散射提供了新思路与有效解决方案。
原文摘要 · Abstract (English)
In this work, we propose a deep learning-based imaging method for addressing the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By combining deep learning technology with EM physical laws, we have successfully developed a multi-frequency neural Born iterative method (NeuralBIM), guided by the principles of the single-frequency NeuralBIM. This method integrates multitask learning techniques with NeuralBIM's efficient iterative inversion process to construct a robust multi-frequency Born iterative inversion model. During training, the model employs a multitask learning approach guided by homoscedastic uncertainty to adaptively allocate the weights of each frequency's data. Additionally, an unsupervised learning method, constrained by the physical laws of ISP, is used to train the multi-frequency NeuralBIM model, eliminating the need for contrast and total field data. The effectiveness of the multi-frequency NeuralBIM is validated through synthetic and experimental data, demonstrating improvements in accuracy and computational efficiency for solving ISP. Moreover, this method exhibits strong generalization capabilities and noise resistance. The multi-frequency NeuralBIM method explores a novel inversion method for multi-frequency EM data and provides an effective solution for the electromagnetic ISP of multi-frequency data.
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