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

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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

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

doi: 10.11999/JEIT160104 cstr: 32379.14.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

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References(19)

References

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