In this paper, we present a framework for the solution of inverse scattering problems that integrates traditional imaging methods and deep learning. The goal is to image piece-wise homogeneous targets and it is pursued in three steps. First, raw-data are processed via orthogonality sampling method to obtain a qualitative image of the targets. Then, such an image is fed into a U-Net. In order to take advantage of the implicitly sparse nature of the information to be retrieved, the network is trained to retrieve a map of the spatial gradient of the unknown contrast. Finally, such an augmented shape is turned into a map of the unknown permittivity by means of a simple post-processing. The framework is computationally effective, since all processing steps are performed in real-time. To provide an example of the achievable performance, Fresnel experimental data have been used as a validation.

中文翻译：

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用于分段均匀目标微波成像的深度学习增强逆散射框架

在本文中，我们提出了一个集成传统成像方法和深度学习的逆散射问题解决框架。目标是对分段同质目标进行成像，并分三个步骤实现。首先，通过正交采样方法处理原始数据以获得目标的定性图像。然后，这样的图像被输入到 U-Net 中。为了利用要检索的信息的隐式稀疏性质，训练网络来检索未知对比度的空间梯度图。最后，这样一个增强形状通过简单的后处理将其转化为未知介电常数的图。该框架在计算上是有效的，因为所有处理步骤都是实时执行的。为了提供可实现性能的示例，菲涅尔实验数据已被用作验证。