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

作　　者：侯兴松[1] 张兰[1] 肖琳[1] 

机构地区：[1]西安交通大学电子与信息工程学院,西安710049

出　　处：《西安交通大学学报》2013年第8期74-79,共6页Journal of Xi'an Jiaotong University

基　　金：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)

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3dB左右。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：合成孔径雷达 方向提升小波变换 稀疏表示 贝叶斯推理 压缩感知 

分 类 号：TN914.42[电子电信—通信与信息系统]