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• W2955306557 startingPage "2254" @default.
• W2955306557 abstract "In this letter, a new deep learning (DL) approach is proposed to solve the electromagnetic inverse scattering (EMIS) problems. The conventional methods for solving inverse problems face various challenges including strong ill-conditions, high contrast, expensive computation cost, and unavoidable intrinsic nonlinearity. To overcome these issues, we propose a new two-step machine learning based approach. In the first step, a complex-valued deep convolutional neural network is employed to retrieve initial contrasts (permittivities) of dielectric scatterers from measured scattering data. In the second step, the previously obtained contrasts are input into a complex-valued deep residual convolutional neural network to refine the reconstruction of images. Consequently, the EMIS problem can be solved with much higher accuracy even for high-contrast objects. Numerical examples have demonstrated the capability of the newly proposed method with the improved accuracy. The proposed DL approach for EMIS problem serves a new path for realizing real-time quantitative microwave imaging for high-contrast objects." @default.
• W2955306557 created "2019-07-12" @default.
• W2955306557 creator A5010694674 @default.
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• W2955306557 date "2019-11-01" @default.
• W2955306557 modified "2023-10-10" @default.
• W2955306557 title "Two-Step Enhanced Deep Learning Approach for Electromagnetic Inverse Scattering Problems" @default.
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• W2955306557 doi "https://doi.org/10.1109/lawp.2019.2925578" @default.
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