用质量因子优化数据与网络，提升电磁逆散射成像精度
Quality-factor inspired deep neural network solver for solving inverse scattering problems
- 引入质量因子筛选训练数据，改进网络特征提取能力
- 融合物理约束的损失函数，显著降低背景伪影
- 在实验中验证成像效果，适合高精度逆散射场景
深度神经网络已应用于电磁逆散射问题（ISPs），表现出优异的成像性能，但受训练数据集、网络结构和损失函数影响。本文提出基于质量因子的数据筛选机制，优化训练数据构成；在网络架构中引入残差连接与通道注意力机制，增强特征提取能力；设计融合数据拟合误差、物理信息约束及解的期望特性的损失函数，有效抑制背景伪影并提升重建精度。通过多种数值分析验证了所提质量因子启发式深度神经网络（QuaDNN）的优势，最终通过实验成像测试验证其成像性能。
原文摘要 · Abstract (English)
Deep neural networks have been applied to address electromagnetic inverse scattering problems (ISPs) and shown superior imaging performances, which can be affected by the training dataset, the network architecture and the applied loss function. Here, the quality of data samples is cared and valued by the defined quality factor. Based on the quality factor, the composition of the training dataset is optimized. The network architecture is integrated with the residual connections and channel attention mechanism to improve feature extraction. A loss function that incorporates data-fitting error, physical-information constraints and the desired feature of the solution is designed and analyzed to suppress the background artifacts and improve the reconstruction accuracy. Various numerical analysis are performed to demonstrate the superiority of the proposed quality-factor inspired deep neural network (QuaDNN) solver and the imaging performance is finally verified by experimental imaging test.
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