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• W3033137211 startingPage "7457" @default.
• W3033137211 abstract "Without phase information of the measured field data, the phaseless data inverse scattering problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared with full data ISPs (FD-ISPs). In this article, we propose a learning-based inversion approach in the frame of the U-net convolutional neural network (CNN) to quantitatively image unknown scatterers located in homogeneous background from the amplitude-only measured total field (also denoted PD). Three training schemes with different inputs to the U-net CNN are proposed and compared, i.e., the direct inversion scheme (DIS) with phaseless total field data, retrieval dominant induced currents by the Levenberg-Marquardt (LM) method (PD-DICs), and PD with contrast source inversion (PD-CSI) scheme. We also demonstrate the setup of training data and compare the performance of the three schemes using both numerical and experimental tests. It is found that the proposed PD-CSI and PD-DICs perform better in terms of accuracy, generalization ability, and robustness compared with DIS. PD-CSI has the strongest capability to tackle with PD-ISPs, which outperforms the PD-DICs and DIS." @default.
• W3033137211 created "2020-06-12" @default.
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• W3033137211 date "2020-11-01" @default.
• W3033137211 modified "2023-10-17" @default.
• W3033137211 title "Deep Learning-Based Inversion Methods for Solving Inverse Scattering Problems With Phaseless Data" @default.
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• W3033137211 doi "https://doi.org/10.1109/tap.2020.2998171" @default.
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