A Novel Plug-and-Play SAR Reconstruction Framework Using Deep Priors

Alver, Muhammed Burak; Saleem, Ammar; Çetin, Müjdat

doi:10.1109/radar.2019.8835598

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…A large number of algorithms have been proposed and developed over the past few decades to solve electromagnetic inverse scattering problem, which can be divided into the following two: (a) deterministic optimization methods including Distorted Born iterative methods (DBIM) [12,13], Subspace based Optimization (SOM) [14][15][16][17][18], Contrast Source Inversion [19][20][21], and (b) stochastic methods [22][23][24] such as Particle Swarm Optimization Algorithms (PSO). In recent years, with the widely studied and rapidly developed of compressive sensing theory , some inverse scattering methods were produced for addressing the problem of Synthetic Aperture Radar(SAR) imaging [25][26][27][28]. Despite it has been verified that these methods can provide satisfactory results for objects of intermediate size and contrast.…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Pix2pix—Deep Neural Network for Nonlinear Electromagnetic Inverse Scattering

Guo

Song

2021

Electronics

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Nonlinear electromagnetic inverse scattering is an imaging technique with quantitative reconstruction and high resolution. Compared with conventional tomography, it takes into account the more realistic interaction between the internal structure of the scene and the electromagnetic waves. However, there are still open issues and challenges due to its inherent strong non-linearity, ill-posedness and computational cost. To overcome these shortcomings, we apply an image translation network, named as Complex-Valued Pix2pix, on the inverse scattering problem of electromagnetic field. Complex-Valued Pix2pix includes two parts of Generator and Discriminator. The Generator employs a multi-layer complex valued convolutional neural network, while the Discriminator computes the maximum likelihoods between the original value and the reconstructed value from the aspects of the two parts of the complex: real part and imaginary part, respectively. The results show that the Complex-Valued Pix2pix can learn the mapping from the initial contrast to the real contrast in microwave imaging models. Moreover, due to the introduction of discriminator, Complex-Valued Pix2pix can capture more features of nonlinearity than traditional Convolutional Neural Network (CNN) by confrontation training. Therefore, without considering the time cost of training, Complex-Valued Pix2pix may be a more effective way to solve inverse scattering problems than other deep learning methods. The main improvement of this work lies in the realization of a Generative Adversarial Network (GAN) in the electromagnetic inverse scattering problem, adding a discriminator to the traditional Convolutional Neural Network (CNN) method to optimize network training. It has the prospect of outperforming conventional methods in terms of both the image quality and computational efficiency.

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