In this letter, a learning-based inversion method with a hybrid input scheme is proposed to solve quantitative microwave imaging (MI) problems. The high-resolution dielectric targets are reconstructed by convolutional neural network (CNN) with a hybrid input scheme. The qualitative direct sampling method (DSM) is utilized to provide the spatial information, while the quantitative back propagation (BP) is employed to get the preliminary constitutive parameters of the unknown target. The hybrid input scheme, defined as an inner product of DSM and BP results (shorten as a DSM-BP scheme), significantly improves the reconstruction quality of U-net CNN compared to the BP-only input scheme without additional computational burden. The accuracy and stability of the proposed inversion are verified by both synthetic and experimental data.

中文翻译：

---

混合输入方案的基于学习的定量微波成像

在这封信中，提出了一种具有混合输入方案的基于学习的反演方法来解决定量微波成像 (MI) 问题。高分辨率介电目标由具有混合输入方案的卷积神经网络 (CNN) 重建。定性直接采样方法（DSM）用于提供空间信息，而定量反向传播（BP）用于获取未知目标的初步本构参数。混合输入方案，定义为 DSM 和 BP 结果的内积（简称为 DSM-BP 方案），与仅 BP 输入方案相比，显着提高了 U-net CNN 的重建质量，没有额外的计算负担。所提出的反演的准确性和稳定性得到了合成和实验数据的验证。