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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

吳新杰, 閆詩雨, 徐攀峰, 顏華. 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[J]. 電子與信息學(xué)報(bào), 2018, 40(5): 1250-1257. doi: 10.11999/JEIT170794
引用本文: 吳新杰, 閆詩雨, 徐攀峰, 顏華. 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[J]. 電子與信息學(xué)報(bào), 2018, 40(5): 1250-1257. doi: 10.11999/JEIT170794
WU Xinjie, YAN Shiyu, XU Panfeng, YAN Hua. Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1250-1257. doi: 10.11999/JEIT170794
Citation: WU Xinjie, YAN Shiyu, XU Panfeng, YAN Hua. Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Sparsity Adaptive Compressed Sensing[J]. Journal of Electronics & Information Technology, 2018, 40(5): 1250-1257. 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
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)

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

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: 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。
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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

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