The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects. In this paper, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation (TV) regularizer. The proposed method is obtained by relating iterations of IBA to layers of a feedforward neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.

翻译：迭代诞生近似(IBA)是描述半透明物体散落的波浪的一种众所周知的方法。 在本文中,我们介绍了一种新型的非线性反向散射方法,将IBA与边缘保留总变异(TV)常规化器结合起来。 拟议的方法是通过将IBA的迭代与进料向神经网络的层相挂钩,并开发相应的错误反向反演算法,以有效估计物体的允许性。 模拟说明,通过计算多种散射,该方法成功地回收了传统线性反向散射失效时的许可分布。