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Embedding Deep Learning in Inverse Scattering Problems
IEEE Transactions on Computational Imaging ( IF 5.4 ) Pub Date : 2020-01-01 , DOI: 10.1109/tci.2019.2915580
Yash Sanghvi , Yaswanth Kalepu , Uday K. Khankhoje

In this paper, we introduce a deep-learning-based framework to solve electromagnetic inverse scattering problems. This framework builds on and extends the capabilities of existing physics-based inversion algorithms. These algorithms, such as the contrast source inversion, subspace-optimization method, and their variants face a problem of getting trapped in false local minima when recovering objects with high permittivity. We propose a novel convolutional neural network architecture, termed the contrast source network, that learns the noise space components of the radiation operator. Together with the signal space components directly estimated from the data, we iteratively refine the solution and show convergence to the correct solution in cases where traditional techniques fail without any significant increase in computational time. We also propose a novel multiresolution strategy that helps in producing high resolution solutions without any significant increase in computational costs. Through extensive numerical experiments, we demonstrate the ability to recover high permittivity objects that include homogeneous, heterogeneous, and lossy scatterers.

中文翻译:

在逆散射问题中嵌入深度学习

在本文中,我们介绍了一种基于深度学习的框架来解决电磁逆散射问题。该框架建立并扩展了现有的基于物理的反演算法的能力。这些算法,例如对比度源反演、子空间优化方法及其变体,在恢复具有高介电常数的对象时面临陷入虚假局部最小值的问题。我们提出了一种新的卷积神经网络架构,称为对比源网络,它学习辐射算子的噪声空间分量。与直接从数据中估计的信号空间分量一起,我们迭代地细化解决方案,并在传统技术失败且计算时间没有任何显着增加的情况下显示收敛到正确的解决方案。我们还提出了一种新颖的多分辨率策略,有助于在不显着增加计算成本的情况下生成高分辨率解决方案。通过大量的数值实验,我们证明了恢复高介电常数物体的能力,包括均匀、异质和有损散射体。
更新日期:2020-01-01
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