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| Block-Sparse Reconstruction for Electrical Impedance Tomography |
| WANG Qi①② ZHANG Pengcheng①② WANG Jianming①② LI Xiuyan①② LIAN Zhijie①② CHEN Qingliang③ CHEN Tongyun③ CHEN Xiaojing①② HEJing①② DUAN Xiaojie①② WANG Huaxiang④ |
①(School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)
②(Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, Tianjin 300387, China)
③(Tianjin Chest Hospital, Tianjin 300000, China)
④(School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China) |
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Abstract An electrical impedance image reconstruction algorithm based on adaptive block-sparse dictionary is proposed. A block-sparse dictionary is constructed creatively, which preferably preserves the details of reconstructed images. Meanwhile, the sparsifying dictionary optimization and image reconstruction are performed alternately, and the intermediate result of the iterative reconstruction is used as the training sample of the sparse dictionary, which can effectively improve the learning effect of the dictionary. The numerical simulation and experiment results show that the patch-based sparsity method for measure noise has excellent robustness and can accurately reconstruct the conductivity distribution image, especially the precise details of mutation.
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Received: 09 May 2017
Published: 11 January 2018
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| Fund: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) |
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Corresponding Authors:
WANG Jianming
E-mail: wjm_tjpu@163.com
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| [1] |
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.
|
| [2] |
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.
|
| [3] |
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.
|
| [4] |
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.
|
| [5] |
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.
|
| [6] |
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.
|
| [7] |
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.
|
| [8] |
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.
|
| [9] |
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.
|
| [10] |
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.
|
| [11] |
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.
|
| [12] |
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.
|
| [13] |
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.
|
| [14] |
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.
|
| [15] |
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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