Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder-Decoder Architecture for Electromagnetic Inverse Scattering Problems
This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and s...
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| Vydané v: | IEEE antennas and wireless propagation letters Ročník 19; číslo 7; s. 1211 - 1215 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1536-1225, 1548-5757 |
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| Abstract | This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and strong ill-posedness. To surmount these difficulties, a novel DL approach is proposed based on a novel complex-valued deep fully convolutional neural network structure. The proposed complex-valued deep learning model for solving EMIS problems composes of an encoder network and its corresponding decoder network, followed by a final pixel-wise regression layer. The complex-valued encoder network extracts feature fragments from received scattered field data, while the role of the complex-valued decoder network is mapping the feature fragments to retrieve the final contrasts (permittivities) of dielectric scatterers. Hence, the proposed deep learning model functions as an "heterogeneous" transformation process, where measured scattering field data is converted into the corresponding contrasts of scatterers. As a consequence, the EMIS problem could be resolved accurately even for extremely high-contrast targets. Numerical benchmarks have illustrated the feasibility and accuracy of our proposed approach. The proposed approach opens a novel path for the deep learning-based real-time quantitative microwave imaging for high-contrast scatterers. |
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| AbstractList | This letter proposes a novel deep learning (DL) approach to resolve the electromagnetic inverse scattering (EMIS) problems. The conventional approaches of resolving EMIS problems encounter assorted difficulties, such as high contrast, high computational cost, inevitable intrinsic nonlinearity, and strong ill-posedness. To surmount these difficulties, a novel DL approach is proposed based on a novel complex-valued deep fully convolutional neural network structure. The proposed complex-valued deep learning model for solving EMIS problems composes of an encoder network and its corresponding decoder network, followed by a final pixel-wise regression layer. The complex-valued encoder network extracts feature fragments from received scattered field data, while the role of the complex-valued decoder network is mapping the feature fragments to retrieve the final contrasts (permittivities) of dielectric scatterers. Hence, the proposed deep learning model functions as an "heterogeneous" transformation process, where measured scattering field data is converted into the corresponding contrasts of scatterers. As a consequence, the EMIS problem could be resolved accurately even for extremely high-contrast targets. Numerical benchmarks have illustrated the feasibility and accuracy of our proposed approach. The proposed approach opens a novel path for the deep learning-based real-time quantitative microwave imaging for high-contrast scatterers. |
| Author | Sha, Wei E. I. Yao, He Ming Jiang, Lijun |
| Author_xml | – sequence: 1 givenname: He Ming orcidid: 0000-0003-2814-9539 surname: Yao fullname: Yao, He Ming email: yaohmhk@connect.hku.hk organization: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong – sequence: 2 givenname: Lijun orcidid: 0000-0002-7391-6322 surname: Jiang fullname: Jiang, Lijun email: jianglj@hku.hk organization: Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong – sequence: 3 givenname: Wei E. I. orcidid: 0000-0002-7431-8121 surname: Sha fullname: Sha, Wei E. I. email: weisha@zju.edu.cn organization: Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province, College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China |
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| Cites_doi | 10.1109/TIP.2017.2713099 10.1088/0266-5611/21/6/S04 10.1088/0266-5611/29/2/025015 10.1109/8.214608 10.1109/TEMC.2016.2642955 10.1109/TAP.2018.2885437 10.1088/0266-5611/18/2/313 10.1109/TGRS.2016.2551720 10.1201/b17623 10.1109/36.752198 10.1109/TGRS.2018.2869221 10.1364/JOSAA.22.001889 10.1109/TAP.2016.2560901 10.1109/MAP.2017.2731203 10.1007/978-3-319-11179-7_36 10.1109/TMI.2018.2828303 10.1029/2000RS002545 10.1109/LAWP.2018.2885570 10.1038/nature14539 10.1088/0266-5611/27/5/055011 10.1002/9780470602492 10.1109/LGRS.2017.2698213 10.1088/1361-6420/aa9581 10.1007/s11063-015-9420-y 10.1007/978-1-4842-2845-6 10.1109/TPAMI.2016.2644615 10.1109/TAP.2019.2902667 10.1109/LAWP.2019.2925578 10.1103/PhysRevLett.105.255501 10.1109/ACCESS.2019.2915263 10.1109/LAWP.2019.2927543 10.1109/TAP.2012.2189712 10.1038/srep11131 10.2528/PIERM18082907 10.1190/geo2013-0398.1 10.1109/APUSNCURSINRSM.2018.8608745 10.1002/(SICI)1098-1098(199621)7:1<16::AID-IMA2>3.0.CO;2-X 10.1109/APUSNCURSINRSM.2017.8072529 10.1109/22.883861 10.1109/TIP.2006.877507 10.1109/EDAPS.2017.8277017 10.1109/ACCESS.2020.2969569 10.1145/3065386 10.1007/978-3-319-24574-4_28 10.1364/OE.26.014678 |
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| SubjectTerms | Artificial neural networks Coders Convolutional neural network Decoding Deep learning deep learning (DL) Electromagnetic interference electromagnetic inverse scattering (EMIS) Encoding Feature extraction Fragments high-contrast scatterer Inverse scattering Machine learning Mapping Mathematical model Receivers Scattering |
| Title | Enhanced Deep Learning Approach Based on the Deep Convolutional Encoder-Decoder Architecture for Electromagnetic Inverse Scattering Problems |
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