Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

Li Shiyong; Amin Moeness; Zhao Guoqiang; Sun Houjun

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Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

机译：通过稀疏优化结合MRF聚类的雷达成像

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摘要

Recent progress in compressive sensing underscores the importance of exploiting intrinsic structures in sparse signal reconstruction. In this letter, we propose a Markov random field (MRF) prior in conjunction with fast iterative shrinkage-thresholding algorithm (FISTA) for image reconstruction. The MRF prior is used to represent the support of sparse signals with clustered nonzero coefficients. The proposed approach is applied to the inverse synthetic aperture radar (ISAR) imaging problem. Simulations and experimental results are provided to demonstrate the performance advantages of this approach in comparison with the standard FISTA and existing MRF-based methods.

机译：压缩感测的最近进展强调了利用内在结构在稀疏信号重建中的重要性。在这封信中，我们在与快速迭代收缩阈值算法（FISTA）结合使用以进行图像重建之前提出了Markov随机字段（MRF）。 MRF先验用于表示具有集群非零系数的稀疏信号的支持。所提出的方法应用于逆合成孔径雷达（ISAR）成像问题。提供了模拟和实验结果，以证明这种方法的性能优势与标准母址和基于MRF的方法相比。

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来源
《IEEE Geoscience and Remote Sensing Letters》 |2020年第7期|1139-1143|共5页
作者
Li Shiyong; Amin Moeness; Zhao Guoqiang; Sun Houjun;
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作者单位

Beijing Inst Technol Beijing Key Lab Millimeter Wave & Terahertz Techn Beijing 100081 Peoples R China;

Villanova Univ Ctr Adv Commun Villanova PA 19085 USA;

Beijing Inst Technol Beijing Key Lab Millimeter Wave & Terahertz Techn Beijing 100081 Peoples R China;

Beijing Inst Technol Beijing Key Lab Millimeter Wave & Terahertz Techn Beijing 100081 Peoples R China;

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正文语种 eng
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关键词
TV; Radar imaging; Clustering algorithms; Imaging; Convex functions; Sun; Compressed sensing; Compressive sensing (CS); fast iterative shrinkage-thresholding algorithm (FISTA); inverse synthetic aperture radar (ISAR); Markov random field (MRF);

机译：电视;雷达成像;聚类算法;成像;凸函数;太阳;压缩传感;压缩传感（CS）;快速迭代收缩 - 阈值算法（Fista）;逆合成孔径雷达（ISAR）;马尔可夫随机场（MRF）;

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专利

1. Jointly Using Low-Rank and Sparsity Priors for Sparse Inverse Synthetic Aperture Radar Imaging [J] . Qiu Wei, Zhou Jianxiong, Fu Qiang IEEE Transactions on Image Processing . 2020,第1期

机译：联合使用低秩和稀疏先验进行稀疏逆合成孔径雷达成像
2. Fuzzy Clustering With a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images [J] . Maoguo Gong, Linzhi Su, Meng Jia, IEEE Transactions on Fuzzy Systems . 2014,第1期

机译：具有改进的MRF能量函数的模糊聚类，用于合成孔径雷达图像的变化检测
3. Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization [J] . Yuan Yuan, Jianzhe Lin, Qi Wang Cybernetics, IEEE Transactions on . 2016,第12期

机译：通过多任务联合稀疏表示和逐步MRF优化进行高光谱图像分类
4. Radar correlated imaging for extended target by the clustered sparse Bayesian learning with Laplace prior [C] . Tingting Qian, Guanghua Lu, Guochao Wang International conference on digital image processing . 2018

机译：借助拉普拉斯先验的聚类稀疏贝叶斯学习对扩展目标进行雷达相关成像
5. Architecture estimation from sparse images using grammatical shape priors for cultural heritage [D] . Sui, Yunfeng 2011

机译：使用语法形状先验从稀疏图像中进行建筑估计以进行文化遗产
6. Incorporation of Prior Knowledge for Region of Change Imaging from Sparse Scan Data in Image-Guided Surgery [O] . J. Lee, J. W. Stayman, Y. Otake, -1

机译：纳入从图像引导手术中稀疏扫描数据的更改成像区域的先前知识
7. Reveal Difference in Synthetic Aperture Radar Images Implementing Fuzzy Clustering Along With Improved MRF Energy Function and Wavelet Denoising Technique [O] . Alias Hannah Mini, Rekha K.S., Anitha R. 2016

机译：利用改进的MRF能量函数和小波消噪技术实现模糊聚类的合成孔径雷达图像的显示差异
8. Close the Loop: Joint Blind Image Restoration and Recognition with Sparse Representation Prior. [R] . Zhang, H., Yang, J., Zhang, Y., 2011

机译：关闭循环：使用稀疏表示之前的联合盲图像恢复和识别。

Radar Imaging by Sparse Optimization Incorporating MRF Clustering Prior

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