Performance Analysis and Dynamic Evolution of Deep Convolutional Neural Network for Nonlinear Inverse Scattering

TL;DR

This paper evaluates the performance and stability of DeepNIS, a CNN-based method for nonlinear electromagnetic inverse scattering, demonstrating its effectiveness and near-isometry properties through quantitative analysis.

Contribution

It provides a detailed analysis of DeepNIS's performance as a function of network depth and explores its dynamic evolution and near-isometry behavior.

Findings

DeepNIS outperforms traditional methods in image quality and speed.

Performance improves with more layers up to a certain point.

DeepNIS exhibits near-isometry after proper training.

Abstract

The solution of nonlinear electromagnetic (EM) inverse scattering problems is typically hindered by several challenges such as ill-posedness, strong nonlinearity, and high computational costs. Recently, deep learning has been demonstrated to be a promising tool in addressing these challenges. In particular, it is possible to establish a connection between a deep convolutional neural network (CNN) and iterative solution methods of nonlinear EM inverse scattering. This has led to the development of an efficient CNN-based solution to nonlinear EM inverse problems, termed DeepNIS. It has been shown that DeepNIS can outperform conventional nonlinear inverse scattering methods in terms of both image quality and computational time. In this work, we quantitatively evaluate the performance of DeepNIS as a function of the number of layers using structure similarity measure (SSIM) and mean-square error (MSE) metrics. In addition, we probe the dynamic evolution behavior of DeepNIS by examining its near-isometry property. It is shown that after a proper training stage the proposed CNN is near optimal in terms of the stability and generalization ability.