基于稀疏度自適應(yīng)壓縮感知的電容層析成像圖像重建算法

吳新杰; 閆詩雨; 徐攀峰; 顏華

doi:10.11999/JEIT170794

基于稀疏度自適應(yīng)壓縮感知的電容層析成像圖像重建算法

doi: 10.11999/JEIT170794

1.
(遼寧大學(xué)物理學(xué)院沈陽 110036) ②(沈陽工業(yè)大學(xué)信息科學(xué)與工程學(xué)院沈陽 110870)

基金項(xiàng)目:

國家自然科學(xué)基金(61071141)，遼寧省自然科學(xué)基金(20102082)，遼寧省教育廳科研項(xiàng)目(LFW201708)

計(jì)量
- 文章訪問數(shù): 1544
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-08-07
- 修回日期: 2018-01-10
- 刊出日期: 2018-05-19

Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing

1.
(College of Physics, Liaoning University, Shenyang 110036, China)
2.
(School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China)

Funds:

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)

摘要

摘要: 為提高電容層析成像(ECT)系統(tǒng)重建圖像的質(zhì)量，該文提出一種基于改進(jìn)稀疏度自適應(yīng)的壓縮感知電容層析成像算法。利用壓縮感知與電容層析成像算法的契合點(diǎn)，以隨機(jī)改造后的電容層析成像靈敏度矩陣為觀測矩陣，離散余弦基為稀疏基，測量電容值為觀測值，建立模型。利用線性反投影算法(LBP算法)所得圖像預(yù)估原始圖像稀疏度，以預(yù)估稀疏度值作為索引原子初始值進(jìn)行稀疏度自適應(yīng)迭代。改進(jìn)后的稀疏度自適應(yīng)匹配追蹤重構(gòu)算法實(shí)現(xiàn)ECT圖像重建，解決了稀疏度預(yù)估不準(zhǔn)確導(dǎo)致重建圖像精度差的問題。仿真實(shí)驗(yàn)結(jié)果表明，該算法可以有效重建ECT圖像，其成像質(zhì)量優(yōu)于LBP算法、Landweber算法、Tikhonov算法等傳統(tǒng)算法，是研究電容層析成像圖像重建的一種新的方法和手段。
- 圖像重建 /
- 電容層析成像 /
- 稀疏度自適應(yīng) /
- 壓縮感知
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.
- Image reconstruction /
- Electrical Capacitance Tomography (ECT) /
- Sparsity adaptive /
- Compressed Sensing (CS)

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