Microwave Radiation Image Reconstruction Method Based on the Mixed Sparse Basis Dictionary Learning

ZHU Lu; SONG Chao; LIU Yuanyuan; HUANG Zhiqun; WANG Yang

doi:10.11999/JEIT160104

Volume 38 Issue 11

Dec. 2016

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(11): 2724-2730

ZHU Lu, SONG Chao, LIU Yuanyuan, HUANG Zhiqun, WANG Yang. Microwave Radiation Image Reconstruction Method Based on the Mixed Sparse Basis Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2724-2730. doi: 10.11999/JEIT160104

Citation:

ZHU Lu, SONG Chao, LIU Yuanyuan, HUANG Zhiqun, WANG Yang. Microwave Radiation Image Reconstruction Method Based on the Mixed Sparse Basis Dictionary Learning[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2724-2730. doi: 10.11999/JEIT160104

Citation:

PDF( 2148 KB)

Microwave Radiation Image Reconstruction Method Based on the Mixed Sparse Basis Dictionary Learning

doi: 10.11999/JEIT160104

Funds:

The National Natural Science Foundation of China (31101081, 61162015), The Natural Science Foundation of Jiangxi Province (20161BAB202061)

Received Date: 2016-01-21
Rev Recd Date: 2016-08-03
Publish Date: 2016-11-19

Abstract

Abstract

At present, the amount of data collection of microwave radiometric imaging system in one snapshot is massive, so it is difficult to achieve the high spatial resolution by conventional microwave radiation imaging method based on the Nyquist sampling. According to the situations of microwave radiation interferometry conducted in the frequency domain, super sparse interferometry is adopted based on the optimal random Fourier sampling to sparsely project microwave radiation image, reducing the amount of data collection. Considering that the microwave radiation image has the character of compressibility in the total variation and microwave domain, the model of microwave radiation image reconstruction method is proposed based on the learning dictionary of mixed sparse basis of total variation and the wavelet, and the microwave radiation image is reconstructed by the Bregman and alternate direction method. The simulation results show that the proposed algorithm is better than the DLMRI algorithm and GradDLRec algorithm from two aspects of image reconstruction and noise sensitivity.
- Microwave Radiation Image (MRI),
- Super sparse interferometry,
- Mixed orthogonal basis learning dictionary,
- Alternate direction method

FullText(HTML)

References(19)

References

SWIFT C T, LEVINE D M, and RUF C S. Aperture synthesis concepts in microwave remote sensing of the Earth[J]. IEEE Transactions on Microwave Theory and Techniques, 1991, 39(12): 1931-1935. doi: 10.1109/22.106530.

KERR Y H, WALDTEUFEL P, WIGNERON J, et al. The SMOS mission: New tool for monitoring key elements of the global water cycle[J]. Proceedings of the IEEE, 2010, 98(5): 666-687. doi: 10.1109/JPROC.2010.2043032.

DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582.

LIU Y DE, DE VOS M, GLIGORIJEVIC I, et al. Multi-structural signal recovery for biomedical compressive sensing[J]. IEEE Transactions on Biomedical Engineering, 2013, 60(10): 2794-2805. doi: 10.1109/TBME.2013.2264772.

朱路, 刘江锋, 刘媛媛, 等. 基于稀疏采样与级联字典的微波辐射图像重构方法[J]. 微波学报, 2014, 30(6): 41-45.

ZHU Lu, LIU Jiangfeng, LIU Yuanyuan, et al. Microwave radiation image reconstruction method based on the sparse sampling and combined dictionary[J]. Journal of Microwaves, 2014, 30(6): 41-45.

AHARON M, ELAD M, and BRUCKSTEIN A. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4311-4322. doi: 10.1109/TSP.2006.881199.

练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展[J]. 自动化学报, 2015, 41(2): 240-260. doi: 10.16383/ j.aas.2015.c140252.

LIAN Qiusheng, SHI Baoshun, and CHEN Shuzhen. Research advances on dictionary learning models, algorithms and applications[J]. Acta Automatica Sinica, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252.

RAVISHANKAR S and BRESLER Y. MR image reconstruction from highly undersampled k-space data by dictionary learning[J]. IEEE Transactions on Medical Imaging, 2011, 30(5): 1028-1041. doi: 10.1109/TMI.2010.2090538.

LIU Q, WANG S, YING L, et al. Adaptive dictionary learning in sparse gradient domain for image recovery[J]. IEEE Transactions on Image Processing, 2013, 22(12): 4652-4663. doi: 10.1109/TIP.2013.2277798.

HUANG Y, PAISLEY J, LIN Q, et al. Bayesian nonparametric dictionary learning for compressed sensing MRI[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5007-5019. doi: 10.1109/TIP.2014.2360122.

THIAGARAJAN J J, RAMAMURTHY K N, and SPANIAS A. Learning stable multilevel dictionaries for space representations[J]. IEEE Transactions on Neural Networks Learning Systems, 2015, 26(9): 1913-1926. doi: 10.1109/ TNNLS.2014.2361052.

SHEN L, SUN G, HUANG Q, et al. Multi-level discriminative dictionary learning with application to large scale image classification[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3109-3123. doi: 10.1109/TIP.2015.2438548.

LU C, SHI J, and JIA J. Scale adaptive dictionary learning[J]. IEEE Transactions on Image Processing, 2014, 23(2): 837-847. doi: 10.1109/TIP.2013.2287602.

MAHMOUD N, FAEZEH Y, and HUSEYIN O. A strategy for residual component-based multiple structured dictionary learning[J]. IEEE Signal Processing Letters, 2015, 22(11): 2059-2063. doi: 10.1109/LSP.2015.2456071.

朱路, 陈素华, 刘江锋, 等. 基于变密度稀疏采样的微波辐射干涉测量反演成像方法[J]. 计算机应用研究, 2015, 32(4): 1236-1239. doi: 10.3969/j.issn.1001-3695.2015.04.066.

ZHU Lu, CHEN Suhua, LIU Jiangfeng, et al. Microwave radiation interferometry inversion imaging method based on variable density sparse sampling[J]. Application Research of Computers, 2015, 32(4): 1236-1239. doi: 10.3969/j.issn. 1001-3695.2015.04.066.

YIN W, OSHER S, GOLDFARB D, et al. Bregman iterative algorithms for l1-minimization with applications to compressed sensing[J]. SIAM Journal on Imaging Sciences, 2008, 1(1): 143-168. doi: 10.1137/070703983.

Relative Articles

Supplements(0)

Cited By

Proportional views