Nonlinear electromagnetic (EM) inverse scattering is a quantitative and super-resolution imaging technique, in which more realistic interactions between the internal structure of scene and EM wavefield are taken into account in the imaging procedure, in contrast to conventional tomography. However, it poses important challenges arising from its intrinsic strong nonlinearity, ill-posedness, and expensive computational costs. To tackle these difficulties, we, for the first time to our best knowledge, exploit a connection between the deep neural network (DNN) architecture and the iterative method of nonlinear EM inverse scattering. This enables the development of a novel DNN-based methodology for nonlinear EM inverse problems (termed here DeepNIS). The proposed DeepNIS consists of a cascade of multilayer complex-valued residual convolutional neural network modules. We numerically and experimentally demonstrate that the DeepNIS outperforms remarkably conventional nonlinear inverse scattering methods in terms of both the image quality and computational time. We show that DeepNIS can learn a general model approximating the underlying EM inverse scattering system. It is expected that the DeepNIS will serve as powerful tool in treating highly nonlinear EM inverse scattering problems over different frequency bands, which are extremely hard and impractical to solve using conventional inverse scattering methods.

The solution of electromagnetic (EM) inverse scattering problems is typically hindered by several challenges such as ill-posedness, 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 EM inverse scattering. This has led to the development of an efficient CNN-based solution to EM inverse problems, termed DeepNIS. It has been shown that DeepNIS can outperform conventional inverse scattering methods in terms of both the 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.

Electromagnetic inverse scattering is a non-invasive examination tool, which holds the promising potential in science, engineering, and military applications. In contrast to conventional tomography techniques, the inverse scattering is a quantitative super-resolution imaging method since it is capable of accommodating more realistic interactions between the wavefield and the probed scene. In this paper, a full probabilistic formulation of the electromagnetic inverse scattering is presented for the first time, which is then solved by applying the well-known expectation maximization method. Afterwards, the concept of complex-valued alternating direction method of multipliers (ADMM) has been proposed as an alternative approach to solve the resulting non-linear optimization problem. Finally, exemplary numerical and experimental results are provided to validate the proposed method.

Three-dimensional multistatic imaging is a powerful noninvasive examination tool for many military and civilian applications. Recently, the sparsity-regularized optimization has been used as a popular imaging technique to enhance the image quality. However, it suffers from the expensive computational cost, since its solution is obtained by a time-consuming iterative scheme, which is typically computationally prohibitive for large-scale imaging problems. To overcome this difficulty, this challenging imaging problem is converted into an image processing problem in this letter, which can be performed over small-scale overlapping patches and be efficiently solved in a parallel or distributed manner. In this way, the proposed qualitative scheme could be utilized to solve large-scale imaging problems. Exemplary simulation results are provided to demonstrate the efficiency of the proposed methodology.

Electromagnetic inverse scattering problem is a typical complex problem while traditional deep convolutional neural network can only be applied to real problem. Motivated by this, this paper presents a new approach for electromagnetic inverse problem with complex convolutional neural network. In this way, several cascaded convolutional neural network modules are introduced to learn a model to realize super-resolution for electromagnetic imaging. The simulation and experimental results show that the proposed method paves a new way addressing real-time practical large-scale electromagnetic inverse scattering problems.

We describe a deep convolutional neural network (CNN) approach for nonlinear electromagnetic (EM) inverse scattering problems. We evaluate the performance of the proposed CNN as a function of the number of layers using different metrics such as structure similarity measure and mean-square error. The results show that the proposed DNN architecture has great potential in tackling nonlinear inverse scattering problems.