Super-resolution reconstruction of radio tomographic image

Cheng Sun; Fei Gao; Heng Liu

doi:10.1109/VTCSpring.2016.7504499

Super-resolution reconstruction of radio tomographic image

Cheng Sun, Fei Gao, Heng Liu

信息与电子学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

To improve the resolution of radio tomographic image and get more details of the target, this paper introduces nonuniform interpolation, projection onto convex sets and structure-adaptive normalized convolution approaches for image reconstruction from a series of radio tomographic images. We compare these approaches in three aspects: time consumption, visual inspection and similarity calculation. Experimental results show that super-resolution reconstruction algorithms enhance the resolution of radio tomographic image while preserving the detail and edge of image, and the structure-adaptive normalized convolution approach is appropriate to radio tomographic imaging system.

源语言	英语
主期刊名	2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781509016983
DOI	https://doi.org/10.1109/VTCSpring.2016.7504499
出版状态	已出版 - 5 7月 2016
活动	83rd IEEE Vehicular Technology Conference, VTC Spring 2016 - Nanjing, 中国期限: 15 5月 2016 → 18 5月 2016

出版系列

姓名	IEEE Vehicular Technology Conference
卷	2016-July
ISSN（印刷版）	1550-2252

会议

会议	83rd IEEE Vehicular Technology Conference, VTC Spring 2016
国家/地区	中国
市	Nanjing
时期	15/05/16 → 18/05/16

访问文件

10.1109/VTCSpring.2016.7504499

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链接到 Scopus 的出版物

引用此

@inproceedings{59fe37a0c3984f5ab6e5d8fe8dfcc326,

title = "Super-resolution reconstruction of radio tomographic image",

abstract = "To improve the resolution of radio tomographic image and get more details of the target, this paper introduces nonuniform interpolation, projection onto convex sets and structure-adaptive normalized convolution approaches for image reconstruction from a series of radio tomographic images. We compare these approaches in three aspects: time consumption, visual inspection and similarity calculation. Experimental results show that super-resolution reconstruction algorithms enhance the resolution of radio tomographic image while preserving the detail and edge of image, and the structure-adaptive normalized convolution approach is appropriate to radio tomographic imaging system.",

keywords = "Image resolution, Radio tomographic imaging, Structure-adaptive normalized convolution, Super-resolution",

author = "Cheng Sun and Fei Gao and Heng Liu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 83rd IEEE Vehicular Technology Conference, VTC Spring 2016 ; Conference date: 15-05-2016 Through 18-05-2016",

year = "2016",

month = jul,

day = "5",

doi = "10.1109/VTCSpring.2016.7504499",

language = "English",

series = "IEEE Vehicular Technology Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings",

address = "United States",

}

Sun, C, Gao, F & Liu, H 2016, Super-resolution reconstruction of radio tomographic image. 在 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings., 7504499, IEEE Vehicular Technology Conference, 卷 2016-July, Institute of Electrical and Electronics Engineers Inc., 83rd IEEE Vehicular Technology Conference, VTC Spring 2016, Nanjing, 中国, 15/05/16. https://doi.org/10.1109/VTCSpring.2016.7504499

Super-resolution reconstruction of radio tomographic image. / Sun, Cheng; Gao, Fei; Liu, Heng.
2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 7504499 (IEEE Vehicular Technology Conference; 卷 2016-July).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Super-resolution reconstruction of radio tomographic image

AU - Sun, Cheng

AU - Gao, Fei

AU - Liu, Heng

PY - 2016/7/5

Y1 - 2016/7/5

N2 - To improve the resolution of radio tomographic image and get more details of the target, this paper introduces nonuniform interpolation, projection onto convex sets and structure-adaptive normalized convolution approaches for image reconstruction from a series of radio tomographic images. We compare these approaches in three aspects: time consumption, visual inspection and similarity calculation. Experimental results show that super-resolution reconstruction algorithms enhance the resolution of radio tomographic image while preserving the detail and edge of image, and the structure-adaptive normalized convolution approach is appropriate to radio tomographic imaging system.

AB - To improve the resolution of radio tomographic image and get more details of the target, this paper introduces nonuniform interpolation, projection onto convex sets and structure-adaptive normalized convolution approaches for image reconstruction from a series of radio tomographic images. We compare these approaches in three aspects: time consumption, visual inspection and similarity calculation. Experimental results show that super-resolution reconstruction algorithms enhance the resolution of radio tomographic image while preserving the detail and edge of image, and the structure-adaptive normalized convolution approach is appropriate to radio tomographic imaging system.

KW - Image resolution

KW - Radio tomographic imaging

KW - Structure-adaptive normalized convolution

KW - Super-resolution

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U2 - 10.1109/VTCSpring.2016.7504499

DO - 10.1109/VTCSpring.2016.7504499

M3 - Conference contribution

AN - SCOPUS:84979737376

T3 - IEEE Vehicular Technology Conference

BT - 2016 IEEE 83rd Vehicular Technology Conference, VTC Spring 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 83rd IEEE Vehicular Technology Conference, VTC Spring 2016

Y2 - 15 May 2016 through 18 May 2016

ER -

Super-resolution reconstruction of radio tomographic image

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