Image Recovery for Electrical Capacitance Tomography Based on Low-Rank Decomposition

被引：26
|
作者：
Ye, Jiamin [1 ]
Wang, Haigang [1 ]
Yang, Wuqiang [2 ]
机构：
[1] Chinese Acad Sci, Inst Engn Thermophys, Beijing 100190, Peoples R China
[2] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England
来源：
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2017年 / 66卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Electrical capacitance tomography; image recovery; low-rank matrix; robust principal component analysis; sparse errors; RECONSTRUCTION ALGORITHMS; MATRIX DECOMPOSITION; DYNAMIC MRI; SENSOR;
D O I：
10.1109/TIM.2017.2664458
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Images reconstructed by an existing image reconstruction algorithm for electrical capacitance tomography (ECT) are usually blurred or corrupted due to data noise. To recover the missing or corrupted pixels in the images, an approach to robustly reconstruct images based on low-rank decomposition is presented for ECT. With this approach, recovering the permittivity distribution is recast as one of recovering low-rank matrix from corrupted images. A convex optimization technique is used to obtain the correct low-rank matrix and the error matrix. Experimental results show the effectiveness of the proposed method. Furthermore, the simplified optimization method is fast and could be used for online imaging. The method can also be used in other tomographic techniques.
引用
收藏
页码：1751 / 1759
页数：9
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