论文标题
采样类型方法与深度学习与一个入射波相反的散射结合
Sampling type method combined with deep learning for inverse scattering with one incident wave
论文作者
论文摘要
我们考虑确定可渗透对象的几何形状从一个固定频率以固定频率生成的散射数据中的几何形状的逆问题。我们首先研究一种正交采样类型方法，该方法快速，易于实现，并且对数据中的噪声进行了鲁棒性。该采样方法具有一种新的成像功能，可用于在近场或远场区域测量的数据。在建立功能的显式衰减率的情况下，分析了成像功能的分辨率分析。还研究了Potthast与正交采样方法的连接。然后将采样方法与深神网络结合使用，以解决反散射问题。使用采样方法计算的第一层的采样方法计算的图像可以将此组合方法理解为网络，然后是其余层的U-NET体系结构。快速计算和来自采样方法结果的知识有助于加快网络的训练。该组合导致最初通过采样方法获得的重建结果的显着改善。合并的方法还能够扭转一些有限的光圈实验数据，而无需进行任何其他转移训练。
We consider the inverse problem of determining the geometry of penetrable objects from scattering data generated by one incident wave at a fixed frequency. We first study an orthogonality sampling type method which is fast, simple to implement, and robust against noise in the data. This sampling method has a new imaging functional that is applicable to data measured in near field or far field regions. The resolution analysis of the imaging functional is analyzed where the explicit decay rate of the functional is established. A connection with the orthogonality sampling method by Potthast is also studied. The sampling method is then combined with a deep neural network to solve the inverse scattering problem. This combined method can be understood as a network using the image computed by the sampling method for the first layer and followed by the U-net architecture for the rest of the layers. The fast computation and the knowledge from the results of the sampling method help speed up the training of the network. The combination leads to a significant improvement in the reconstruction results initially obtained by the sampling method. The combined method is also able to invert some limited aperture experimental data without any additional transfer training.
