用物理约束提升反散射成像效率，兼顾精度与泛化能力
Contrast-Source-Based Physics-Driven Neural Network for Inverse Scattering Problems
- 基于对比源构建物理驱动神经网络，融合先验知识与测量数据
- 引入自适应总变差损失，在不同对比度和噪声下实现鲁棒重建
- 无需大量训练数据，推理速度快，适合实际应用中的快速成像
深度神经网络（DNN）因强大的非线性映射能力被用于反散射问题（ISPs），但监督学习方法依赖大规模数据集，限制了其泛化能力。无训练神经网络（UNNs）通过从实测电场和先验物理知识中更新权重来解决此问题，但现有方法推理时间长。本文提出一种基于对比源的物理驱动神经网络（CSPDNN），通过预测诱导电流分布提升效率，并引入自适应总变差损失，在不同对比度和噪声条件下实现稳健重建。数值模拟与实验数据验证了其优越成像性能。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) have recently been applied to inverse scattering problems (ISPs) due to their strong nonlinear mapping capabilities. However, supervised DNN solvers require large-scale datasets, which limits their generalization in practical applications. Untrained neural networks (UNNs) address this issue by updating weights from measured electric fields and prior physical knowledge, but existing UNN solvers suffer from long inference time. To overcome these limitations, this paper proposes a contrast-source-based physics-driven neural network (CSPDNN), which predicts the induced current distribution to improve efficiency and incorporates an adaptive total variation loss for robust reconstruction under varying contrast and noise conditions. The improved imaging performance is validated through comprehensive numerical simulations and experimental data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。