中国物理B ›› 2014, Vol. 23 ›› Issue (5): 54203-054203.doi: 10.1088/1674-1056/23/5/054203

• ELECTROMAGNETISM, OPTICS, ACOUSTICS, HEAT TRANSFER, CLASSICAL MECHANICS, AND FLUID DYNAMICS • 上一篇    下一篇

Correspondence normalized ghost imaging on compressive sensing

赵生妹, 庄鹏   

  1. Institute of Signal Processing & Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • 收稿日期:2013-08-21 修回日期:2013-10-22 出版日期:2014-05-15 发布日期:2014-05-15
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 61271238), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20123223110003), and the University Natural Science Research Foundation of Jiangsu Province, China (Grant No. 11KJA510002).

Correspondence normalized ghost imaging on compressive sensing

Zhao Sheng-Mei (赵生妹), Zhuang Peng (庄鹏)   

  1. Institute of Signal Processing & Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Received:2013-08-21 Revised:2013-10-22 Online:2014-05-15 Published:2014-05-15
  • Contact: Zhao Sheng-Mei E-mail:zhaosm@njupt.edu.cn
  • About author:42.50.Ar; 42.30.Wb; 42.25.Kb; 42.30.Va
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 61271238), the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20123223110003), and the University Natural Science Research Foundation of Jiangsu Province, China (Grant No. 11KJA510002).

摘要: Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale “double-slit” image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale “lena” object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale “double-slit” object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time.

关键词: ghost imaging, compressive sensing, time-correspondence, normalizing

Abstract: Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios (SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing (CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing (CS) and time-correspondence imaging (CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale “double-slit” image, the mean square error (MSE) by GI and the normalized GI (NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021 by CCNGI scheme with 2500 measurements. For the eight-grayscale “lena” object, the peak signal-to-noise rates (PSNRs) are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale “double-slit” object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints (GISC) with a shorter reconstruction time.

Key words: ghost imaging, compressive sensing, time-correspondence, normalizing

中图分类号: 

  • 42.50.Ar
42.30.Wb (Image reconstruction; tomography) 42.25.Kb (Coherence) 42.30.Va (Image forming and processing)