基于压缩感知归一化关联成像实现目标重构

郭树旭; 张驰; 曹军胜; 钟菲; 郜峰利

doi:10.3788/OPE.20152301.0288

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基于压缩感知归一化关联成像实现目标重构

信息科学 | 更新时间：2020-08-13

- 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
- 光学精密工程 2015年23卷第1期页码：288-294
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 长春工程学院,吉林长春,130012
  3. 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
- 作者简介：
- 基金信息：
  
  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
- DOI：10.3788/OPE.20152301.0288
  中图分类号： TP391.4
- 收稿日期：2014-08-25，
  
  修回日期：2014-10-17，
  
  纸质出版日期：2015-01-25
- 稿件说明：
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郭树旭, 张驰, 曹军胜等. 基于压缩感知归一化关联成像实现目标重构[J]. 光学精密工程, 2015,23(1): 288-294

GUO Shu-xu, ZHANG Chi, CAO Jun-sheng etc. Object reconstruction by compressive sensing based normalized ghost imaging[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 288-294
郭树旭, 张驰, 曹军胜等. 基于压缩感知归一化关联成像实现目标重构[J]. 光学精密工程, 2015,23(1): 288-294 DOI： 10.3788/OPE.20152301.0288.

GUO Shu-xu, ZHANG Chi, CAO Jun-sheng etc. Object reconstruction by compressive sensing based normalized ghost imaging[J]. Editorial Office of Optics and Precision Engineering, 2015,23(1): 288-294 DOI： 10.3788/OPE.20152301.0288.

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.

Abstract

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

Keywords

references

PITTMAN T B, SHIH Y H, STREKALOV D V, et al.. Optical imaging by means of two-photon quantum entanglement [J]. Physical Review A, 1995, 52(5): R3429.

MEYERS R E, DEACON K S. Quantum ghost imaging experiments at ARL [C].SPIE Optical Engineering+ Applications. International Society for Optics and Photonics, 2010: 78150I-78150I-8.

GATTI A, BRAMBILLA E, BACHE M, et al.. Ghost imaging with thermal light: comparing entanglement and classical correlation [J]. Physical Review Letters, 2004, 93(9): 093602.

SHAPIRO J H. Computational ghost imaging [J]. Physical Review A, 2008, 78(6): 061802.

FERRI F, MAGATTI D, LUGIATO L A, et al.. Differential ghost imaging [J]. Physical Review Letters, 2010, 104(25): 253603.

SUN B, WELSH S S, EDGAR M P, et al.. Normalized ghost imaging [J]. Optics Express, 2012, 20(15): 16892-16901.

CHEN X H, AGAFONOV I N, LUO K H, et al.. High-visibility, high-order lensless ghost imaging with thermal light [J]. Optics Letters, 2010, 35(8): 1166-1168.

KATZ O, BROMBERG Y, SILBERBERG Y. Compressive ghost imaging [J]. Applied Physics Letters, 2009, 95(13): 131110.

陆明海, 沈夏, 韩申生. 基于数字微镜器件的压缩感知关联成像研究 [J]. 光学学报,2011, 31(7): 98-103. LU M H, SHEN X, HAN SH SH. Ghost imaging via compressive sampling based on digital micromirror device [J]. Acta Optica Sinica, 2011, 31(7): 98-103.(in Chinese)

DU J, GONG W, HAN S. The influence of sparsity property of images on ghost imaging with thermal light [J]. Optics Letters, 2012, 37(6): 1067-1069.

白旭, 李永强, 赵生妹. 基于压缩感知的差分关联成像方案研究 [J]. 物理学报, 2013, 62(4): 044209-7. BAI X, LI Y Q, ZHAO SH M. Differential compressive correlated imaging [J]. Acta Physica Sinica, 2013, 62(4): 044209-7.(in Chinese)

BROMBERG Y, KATZ O, SILBERBERG Y. Ghost imaging with a single detector [J]. Physical Review A, 2009, 79(5): 053840.

XIONG J, CAO D Z, HUANG F, et al.. Experimental observation of classical subwavelength interference with a pseudothermal light source [J]. Physical review letters, 2005, 94(17): 173601.

ZHAI Y H, CHEN X H, ZHANG D, et al.. Two-photon interference with true thermal light [J]. Physical Review A, 2005, 72(4): 043805.

孙永明, 吴谨, 刘劲. 基于CS测量矩阵优化的图像融合 [J]. 液晶与显示, 2014, 29(3): 461-465. SUN Y M, WU J, LIU J.Image fusion based on CS measurement matrix optimization [J].Chinese Journal of Liquid Crystals and Displays, 2014, 29(3): 461-465.(in Chinese)

DONOHO D L. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.

朱秋平, 颜佳, 张虎, 等. 基于压缩感知的多特征实时跟踪 [J]. 光学精密工程, 2013, 21(2): 437-444. ZHU Q P, YAN J, ZHANG H, et al.. Real-time tracking using multiple features based on compressive sensing [J]. Opt. Precision Eng., 2013, 21(2): 437-444.(in Chinese)

刘欣悦, 董磊, 王建立. 稀疏采样傅里叶望远镜成像 [J]. 光学精密工程, 2010, 18(3): 521-527. LIU X Y, DONG L, WANG J L. Fourier telescopy imaging via sparse sampling [J]. Opt. Precision Eng., 2010, 18(3): 521-527.(in Chinese)

CANDES E J, ROMBERG J K, TAO T. Stable signal recovery from incomplete and inaccurate measurements [J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207-1223.

CANDES E, ROMBERG J. Sparsity and incoherence in compressive sampling [J]. Inverse Problems, 2007, 23(3): 969.

CANDÈS E J, WAKIN M B. An introduction to compressive sampling [J]. Signal Processing Magazine, IEEE, 2008, 25(2): 21-30.

LIANG D, ZHANG H F, YING L. Compressed-sensing photoacoustic imaging based on random optical illumination [J]. International Journal of Functional Informatics and Personalised Medicine, 2009, 2(4): 394-406.

ZAMBRANO-NUNEZ M, MARENGO E A, FISHER J M. Coherent single-detector imaging system [C].2010 IEEE Workshop on Signal Processing Systems (SIPS), 2010: 111-115.

俞文凯, 姚旭日, 刘雪峰, 等. 压缩传感用于极弱光计数成像 [J]. 光学精密工程, 2012, 20(10): 2283-2292. YU W K, YAO X R, LIU X F, et al.. Compressed sensing for ultra-weak light counting imaging [J]. Opt. Precision Eng., 2012, 20(10): 2283-2292.(in Chinese)

DUNCAN D D, KIRKPATRICK S J. Algorithms for simulation of speckle (laser and otherwise) [C].Biomedical Optics (BiOS) 2008, International Society for Optics and Photonics, 2008,6855: 685505-685505-8.

CANDES E J. The restricted isometry property and its implications for compressed sensing [J]. Comptes Rendus Mathematique, 2008, 346(9-10): 589-592.

TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666.

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