基于压缩感知归一化关联成像实现目标重构  

Object reconstruction by compressive sensing based normalized ghost imaging

作　　者：郭树旭[1] 张驰[1] 曹军胜[2] 钟菲[3] 郜峰利[1] 

机构地区：[1]吉林大学集成光电子学国家重点联合实验室电子科学与工程学院,吉林长春130012 [2]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [3]长春工程学院,吉林长春130012

出　　处：《光学精密工程》2015年第1期288-294,共7页Optics and Precision Engineering

基　　金：国家自然科学基金青年科学基金资助项目(No.61204055);吉林省科技发展计划青年科研基金资助项目(No.20130522188JH);吉林省科技发展计划自然科学基金资助项目(No.20140101175JC)

摘　　要：在归一化关联成像的基础上,结合压缩感知理论,提出了基于压缩感知的归一化关联成像方法.该方法首先对物臂的桶探测值进行归一化处理,并由散斑场构造测量矩阵;然后采用正交匹配追踪算法,在低测量次数下优质量地还原出了物体的像.实验中采用灰度图像及二值图像作为成像目标,以峰值信噪比作为衡量标准,分别对传统关联成像,归一化关联成像及压缩感知归一化关联成像的重构效果进行了量化对比.仿真实验结果表明,对于细节较为丰富的灰度图像,压缩感知归一化关联成像的峰值信噪比较传统方法高6 dB左右,比归一化关联成像方法提高了2 dB左右;对于细节较少的二值图像,其峰值信噪比较归一化关联成像法高3.4～4.3 dB,比传统法高5.2～6.5 dB.最后,采用实际电荷耦合元件测得的散斑场构造了测量矩阵,实验结果进一步验证了基于压缩感知的归一化关联成像算法能提高重构质量.According to compressive sensing theory,a compressive sensing based normalized ghost imaging method was proposed.Firstly,the measurements of a bucket detector were normalized,and the measurement matrix was constructed with speckle fields.Then,the object image was reconstructed with a low number of measurements by adopting orthogonal matching pursuit method.Several experiments were performed by using gray-scale images and binary images respectively as the imaging targets and the Peak Signal to Noise Ratio （PSNR） as the yardstick.The reconstruction effects were quantized and compared for traditional Ghost Imaging（GI）,Normalized Ghost Imaging （NGI） and Compressive Sensing based Normalized Ghost Imaging （CSNGI） respectively.The simulation results indicate that the PSNR of CSNGI is about 6 dB and 2 dB higher than those of GI and NGI on gray-scale images with more details,and 3.4-4.3 dB and 5.2-6.5 dB higher than those of NGI and GI for binary images with less details,respectively.Finally,the actual speckle field measured by Charge Coupled Devices（CCDs） was used to construct the measurement matrix,and the experiment results also further indicate that the CSNGI improves the reconstruction quality greatly.

关 键 词：关联成像 压缩感知 峰值信噪比 图像重构 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]