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基于块稀疏的电阻抗成像算法

王琦 张鹏程 汪剑鸣 李秀艳 连志杰 陈庆良 陈彤云 陈晓静 贺静 段晓杰 王化祥

王琦, 张鹏程, 汪剑鸣, 李秀艳, 连志杰, 陈庆良, 陈彤云, 陈晓静, 贺静, 段晓杰, 王化祥. 基于块稀疏的电阻抗成像算法[J]. 电子与信息学报, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
引用本文: 王琦, 张鹏程, 汪剑鸣, 李秀艳, 连志杰, 陈庆良, 陈彤云, 陈晓静, 贺静, 段晓杰, 王化祥. 基于块稀疏的电阻抗成像算法[J]. 电子与信息学报, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics and Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425
Citation: WANG Qi, ZHANG Pengcheng, WANG Jianming, LI Xiuyan, LIAN Zhijie, CHEN Qingliang, CHEN Tongyun, CHEN Xiaojing, HE Jing, DUAN Xiaojie, WANG Huaxiang. Block-Sparse Reconstruction for Electrical Impedance Tomography[J]. Journal of Electronics and Information Technology, 2018, 40(3): 676-682. doi: 10.11999/JEIT170425

基于块稀疏的电阻抗成像算法

doi: 10.11999/JEIT170425
基金项目: 

国家科技支撑计划重点项目(2013BAF06B00),国家自然科学基金(61601324, 61373104, 61402330, 61405143),天津市应用基础与前沿技术研究计划(15JCQNJC01500)

Block-Sparse Reconstruction for Electrical Impedance Tomography

Funds: 

The Key Projects of National Science and Technology Support Program (2013BAF06B00), The National Natural Science Foundation of China (61601324, 61373104, 61402330, 61405143), The Natural Science Foundation of Tianjin Municipal Science and Technology Commission (15JCQNJC01500)

  • 摘要: 该文提出一种基于自适应块稀疏字典学习的电阻抗图像重建算法,构建了分块稀疏字典,较好地保留了重建图像的细节信息;同时,将字典学习与图像重建交替进行,并将迭代重建的中间结果作为稀疏字典的训练样本,有效提高了字典学习效果。数值仿真与实验重建结果表明,新方法对电阻抗成像系统测量噪声具有较好的鲁棒性,能准确重构电导率分布图像,特别是对突变细节的准确恢复。
  • WANG Q, LIAN Z, WANG J, et al. Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation[J]. Review of Scientific Instruments, 2016, 87(11): 114707, doi: 10.1063/1.4966998.
    SBARBARO D, VAUHKONEN M, and JOHANSEN T A. State estimation and inverse problems in electrical impedance tomography: observability, convergence and regularization[J]. Inverse Problems, 2015, 31(4): 045004, doi: 10.1088/0266- 5611/31/4/045004.
    Ye J, WANG H, and YANG W. Image reconstruction for electrical capacitance tomography based on sparse representation[J]. IEEE Transactions on Instrumentation Measurement, 2015, 64(1): 89-102. doi: 10.1109/TIM.2014. 2329738.
    LIU Y, YANG Z, and YANG L. Online signature verification based on DCT and sparse representation[J]. IEEE Transactions on Cybern, 2015, 45(11): 2498-2511. doi: 10.1109/TCYB.2014.2375959.
    NAZZAL M and OZKARAMANLI H. Wavelet domain dictionary learning-based single image superresolution[J]. Signal, Image and Video Processing, 2015, 1(7): 1-11. doi: 10.1007/s11760-013-0602-7.
    WIECZOREK M, FRIKEL J, VOGEL J, et al. X-ray computed tomography using curvelet sparse regularization[J]. Medical Physics, 2015, 42(4): 1555-1567. doi: 10.1118/ 1.4914368.
    LIU Y, LIU S, and WANG Z. A general framework for image fusion based on multi-scale transform and sparse representation[J]. Information Fusion, 2015, 24: 147-164. doi: 10.1016/j.inffus.2014.09.004.
    GARDE H and KNUDSEN K. Sparsity prior for electrical impedance tomography with partial data[J]. Inverse Problems in Science and Engineering, 2016(3): 524-541. doi: 10.1080/17415977.2015.1047365.
    JIN B, KHAN T, and MAASS P. A reconstruction algorithm for electrical impedance tomography based on sparsity regularization[J]. International Journal for Numerical Methods in Engineering, 2012, 89(3): 337-353. doi: 10.1002 /nme.3247.
    WANG Q, WANG H, ZHANG R, et al. Image reconstruction based on L1 regularization and projection methods for electrical impedance tomography[J]. Review of Scientific Instruments, 2012, 83(10): 104707. doi: 10.1063/1.4760253.
    YUE B, WANG S, LIANG X, et al. Robust coupled dictionary learning with 1-norm coefficients transition constraint for noisy image super-resolution[J]. Signal Processing, 2017, 140: 177-189. doi: 10.1016/j.sigpro.2017. 04.015.
    QU X, HOU Y, LAM F, et al. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator[J]. Medical Image Analysis, 2014, 18(6): 843-856. do: 10.1016/j.media.2013.09.007.
    HEMMING B, FAGERLUND A, and LASSILA A. Linearized solution to electrical impedance tomography based on the Schur conjugate gradient method[J]. Measurement Science Technology, 2007, 18(11): 3373-3383. doi: 10.1088/0957-0233/18/11/017.
    WANG M. Inverse solutions for electrical impedance tomography based on conjugate gradients methods[J]. Measurement Science Technology, 2001, 13(1): 101-117. doi: 10.1088/0957-0233/13/1/314.
    BAO C, CAI J F, and JI H. Fast sparsity-based orthogonal dictionary learning for image restoration[C]. IEEE International Conference on Computer Vision IEEE, Sydney, 2014: 3384-3391. doi: 10.1109/ICCV.2013.420.
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出版历程
  • 收稿日期:  2017-05-09
  • 修回日期:  2017-12-15
  • 刊出日期:  2018-03-19

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