基于稀疏度自适应压缩感知的电容层析成像图像重建算法

doi:10.11999/JEIT170794

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Abstract In order to improve quality of the reconstructed images of the Electrical Capacitance Tomography (ECT) system, an improved sparsity adaptive matching pursuit compressed sensing algorithm is proposed. Based on the coherence point of Compressed Sensing (CS) theory and ECT, the CS-ECT model is established. In the model, the sensitivity matrix of ECT is designed in a random order to be the observation matrix, the discrete cosine base is used as the sparse base, the capacitance value is measured as the observed value. By using the Linear Back Projection (LBP) algorithm, the sparsity of the estimated images is confirmed. The sparsity can be served as the initial value of the atomic index for sparsity adaptive iteration. The lack of image reconstruction accuracy caused by the inaccurate estimate of sparsity can be solved by the improved sparsity adaptive matching pursuit algorithm. Simulation results indicate that reconstructed images with higher accuracy can be obtained using the improved sparsity adaptive matching pursuit compressed sensing algorithm than the LBP algorithm, Landweber algorithm and Tikhonov algorithm. A new method of ECT reconstruction is provided.

Key words： Image reconstruction Electrical Capacitance Tomography (ECT) Sparsity adaptive Compressed Sensing (CS)

Received: 07 August 2017 Published: 14 March 2018

PACS:

TP391

Fund:The National Natural Science Foundation of China (61071141), The Scientific Research Foundation of Liaoning Province (20102082), The Scientific Research Project of Liaoning Provincial Education Department (LFW201708)

Corresponding Authors: WU Xinjie E-mail: wuxinjie@lnu.edu.cn

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	WU Xinjie
	YAN Shiyu
	XU Panfeng
	YAN Hua

Cite this article:

WU Xinjie,YAN Shiyu,XU Panfeng等. Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing[J]. JEIT, 2018, 40(5): 1250-1257.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170794 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I5/1250

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