合成孔径雷达图像的贝叶斯压缩感知重构算法

侯兴松; 张兰; 肖琳

doi:10.7652/xjtuxb201308013

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合成孔径雷达图像的贝叶斯压缩感知重构算法

更新时间：2025-05-08

- 合成孔径雷达图像的贝叶斯压缩感知重构算法
- A Reconstruction Algorithm with Bayesian Compressive Sensing for Synthetic Aperture Radar Images
- 西安交通大学学报 2013年47卷第8期页码：74-79
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2012-12-28。作者简介: 侯兴松(1972—),男,博士,副教授。基金项目: 国家自然科学基金资助项目(60602024);中央高校基本科研业务费专项资金资助项目(xjj2012023)
- DOI：10.7652/xjtuxb201308013
  中图分类号： TN914.42
- 网络首发：2013-08-10，
  
  纸质出版：2013
- 稿件说明：
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侯兴松, 张兰, 肖琳. 合成孔径雷达图像的贝叶斯压缩感知重构算法[J]. 西安交通大学学报, 2013,47(8):74-79.

A Reconstruction Algorithm with Bayesian Compressive Sensing for Synthetic Aperture Radar Images[J]. 2013, 47(8): 74-79.
侯兴松, 张兰, 肖琳. 合成孔径雷达图像的贝叶斯压缩感知重构算法[J]. 西安交通大学学报, 2013,47(8):74-79. DOI： 10.7652/xjtuxb201308013.

A Reconstruction Algorithm with Bayesian Compressive Sensing for Synthetic Aperture Radar Images[J]. 2013, 47(8): 74-79. DOI： 10.7652/xjtuxb201308013.

摘要

针对目前合成孔径雷达(SAR)图像压缩感知重构算法没有充分利用小波系数相关性的缺点

提出了一种综合利用尺度间衰减性和尺度内方向能量聚集性的SAR图像贝叶斯压缩感知重构算法(DLWT-TDC)。首先采用方向提升小波变换(DLWT)对SAR图像进行稀疏表示

然后在3个高频子带中分别使用3×5、5×3、5×5邻域设计了具有方向和空间局部自适应的先验概率分布模型

最后利用马尔科夫链蒙特卡罗采样的贝叶斯推理恢复出图像的小波系数

进而得到重构图像。实验结果表明

DLWT-TDC算法在采样率为50%～90%下可以提高图像的重构性能

与仅利用尺度间相关性的小波树结构的压缩感知重构算法相比

在90%高采样率下的重构性能可提高3 dB左右。

Abstract

A reconstruction algorithm with Bayesian compressive sensing for synthetic aperture radar(SAR)images(DLWT-TDC)is proposed to solve the problem that the dependencies of wavelet coefficients are not fully exploited by existing compressive sensing(CS)reconstruction algorithms. The new algorithm exploits both the interscale attenuation and the intrascale directional clustering property of the directional lifting wavelet transform(DLWT)coefficients. The DLWT is used for SAR image's sparse representation

and then

3×5、5×3 and 5×5 neighboring blocks are used to design prior probability models with local adaptivity in both the direction and space. Then the Bayesian inference via Markov chain Monte Carlo sampling is used to recover the image's wavelet coefficients and the reconstructed image is generated in turn. Experimental results show that the DLWT-TDC achieves high reconstruction performance when the sampling percentage is in the range from 50% to 90%. Comparisons with the Bayesian tree-structured wavelet compressive sensing algorithm

which only uses the interscale dependencies

show that the proposed algorithm improves the peak-signal-to-noise-ratio by about 3 dB when the sampling percentage is 90%.

关键词

Keywords

references

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