压缩感知在电容层析成像中的应用

发布时间：2018-05-08 16:35

本文选题：电容层析成像(ECT) + 图像重建　；参考：《北京航空航天大学学报》2017年11期

【摘要】：压缩感知(CS)理论是在充分利用信号稀疏性或可压缩性的情况下,对信号进行少量采样即可实现信号的精确重建。本文尝试将CS理论应用于电容层析成像(ECT)图像重建中,首先,使用快速傅里叶变换(FFT)基将原始图像灰度信号进行稀疏化处理;其次,将ECT灵敏度矩阵的各行按随机顺序进行排列,得到ECT系统随机观测矩阵;最后,选取当前普遍使用的基于内点法、梯度投影(GPSR)算法以及贪婪算法的CS图像重建算法进行ECT图像重建,并与线性反投影及Landweber迭代算法进行了对比。仿真实验结果表明:基于CS图像理论的ECT图像重建算法,其重建精度有所提高。本文同时分析了3种CS图像重建算法的优缺点及适用范围。
[Abstract]:The theory of compressed sensing (CSS) is that the accurate reconstruction of signals can be realized by sampling a small amount of signals under the condition of making full use of the sparse or compressible signals. In this paper, we try to apply CS theory to ECT image reconstruction. Firstly, we use fast Fourier transform (FFTT) to sparse the gray signal of the original image. The rows of ECT sensitivity matrix are arranged in random order, and the random observation matrix of ECT system is obtained. The gradient projection (GPP) algorithm and the greedy CS image reconstruction algorithm are used to reconstruct the ECT images. The results are compared with the linear backprojection and Landweber iterative algorithms. The simulation results show that the reconstruction accuracy of ECT image reconstruction algorithm based on CS image theory is improved. At the same time, the advantages and disadvantages of three CS image reconstruction algorithms and their application range are analyzed.
【作者单位】：华北电力大学自动化系;
【基金】：国家自然科学基金(51306058) 中央高校基本科研业务费专项资金(2017MS131)~~
【分类号】：TP391.41

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