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基于混合稀疏基字典学习的微波辐射图像重构方法

朱路 宋超 刘媛媛 黄志群 王杨

朱路, 宋超, 刘媛媛, 黄志群, 王杨. 基于混合稀疏基字典学习的微波辐射图像重构方法[J]. 电子与信息学报, 2016, 38(11): 2724-2730. doi: 10.11999/JEIT160104
引用本文: 朱路, 宋超, 刘媛媛, 黄志群, 王杨. 基于混合稀疏基字典学习的微波辐射图像重构方法[J]. 电子与信息学报, 2016, 38(11): 2724-2730. doi: 10.11999/JEIT160104
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

基于混合稀疏基字典学习的微波辐射图像重构方法

doi: 10.11999/JEIT160104
基金项目: 

国家自然科学基金(31101081, 61162015),江西省自然科学基金(20161BAB202061)

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

Funds: 

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

  • 摘要: 目前的微波辐射测量成像系统在一次观测中所采集的数据量大,基于奈奎斯特空间采样及常规微波辐射图像重构方法难以实现高分辨率要求。该文针对微波辐射干涉测量在频域中进行,采用傅里叶最优随机抽取的超稀疏干涉测量(低于奈奎斯特采样)对微波辐射图像进行线性压缩投影,降低数据采样。考虑微波辐射图像在总体差分域和小波中都具有可压缩特性,提出总体差分和小波混合正交基的K-SVD字典学习微波辐射图像重构模型,利用Bregman和交替迭代算法求解该模型,重构线性压缩投影信息从而获得微波辐射图像。仿真实验表明,该文提出的算法在微波辐射图像重构效果、噪声稳定性上优于DLMRI算法和GradDLRec算法。
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出版历程
  • 收稿日期:  2016-01-21
  • 修回日期:  2016-08-03
  • 刊出日期:  2016-11-19

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