OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 53, Iss. 13 — May. 1, 2014
  • pp: 2924–2928

Application of multi-correlation-scale measurement matrices in ghost imaging via sparsity constraints

Mingliang Chen, Enrong Li, and Shensheng Han  »View Author Affiliations


Applied Optics, Vol. 53, Issue 13, pp. 2924-2928 (2014)
http://dx.doi.org/10.1364/AO.53.002924


View Full Text Article

Enhanced HTML    Acrobat PDF (570 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

Sampling and reconstruction techniques are of special interest and importance in ghost imaging. Up to now, the transverse correlation scale of measurement matrices are usually constant. This paper explores a new possibility of constructing highly efficient measurement matrices with multi-correlation scales. Comparisons between the simulational and experimental results show that the multi-correlation-scale measurement matrices are highly efficient and accurate in sampling and image reconstruction and have a better antinoise ability than the existing constant-correlation-scale measurement matrices.

© 2014 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.3010) Image processing : Image reconstruction techniques
(110.0110) Imaging systems : Imaging systems
(110.1758) Imaging systems : Computational imaging

ToC Category:
Imaging Systems

History
Original Manuscript: January 21, 2014
Revised Manuscript: March 22, 2014
Manuscript Accepted: March 31, 2014
Published: April 30, 2014

Citation
Mingliang Chen, Enrong Li, and Shensheng Han, "Application of multi-correlation-scale measurement matrices in ghost imaging via sparsity constraints," Appl. Opt. 53, 2924-2928 (2014)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-53-13-2924


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. P. Zerom, Z. Shi, M. N. O’Sullivan, K. W. C. Chan, M. Krogstad, J. H. Shapiro, and R. W. Boyd, “Thermal ghost imaging with averaged speckle patterns,” Phys. Rev. A 86, 063817 (2012). [CrossRef]
  2. B. Sun, S. S. Welsh, M. P. Edgar, J. H. Shapiro, and M. J. Padgett, “Normalized ghost imaging,” Opt. Express 20, 16892–16901 (2012). [CrossRef]
  3. W. Gong and S. Han, “A method to improve the visibility of ghost images obtained by thermal light,” Phys. Lett. A 374, 1005–1008 (2010). [CrossRef]
  4. F. Ferri, D. Magatti, L. A. Lugiato, and A. Gatti, “Differential ghost imaging,” Phys. Rev. Lett. 104, 253603 (2010). [CrossRef]
  5. D. Donoho, “Compressed sensing,” IEEE Trans. Inf. Theory 52, 1289–1306 (2006). [CrossRef]
  6. E. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489–509 (2006).
  7. E. J. Candes, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 1207–1223 (2006). [CrossRef]
  8. E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25(2), 21–30 (2008). [CrossRef]
  9. O. Katz, Y. Bromberg, and Y. Silberberg, “Compressive ghost imaging,” Appl. Phys. Lett. 95, 131110 (2009). [CrossRef]
  10. W. Gong and S. Han, “Super-resolution ghost imaging via compressive sampling reconstruction,” arXiv:0910.4823 (2009).
  11. J. Shapiro, “Computational ghost imaging,” Phys. Rev. A 78, 061802 (2008). [CrossRef]
  12. M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, T. Sun, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag. 25(2), 83–91 (2008). [CrossRef]
  13. M. Lu, X. Shen, and S. Han, “Ghost imaging via compressive sampling based on digital micromirror device,” Acta Opt. Sin. 31, 0711002 (2011). [CrossRef]
  14. R. Meyers, K. Deacon, A. Tunick, and Y. Shih, “Virtual ghost imaging through turbulence and obscurants using bessel beam illumination,” Appl. Phys. Lett. 100, 061126 (2012). [CrossRef]
  15. C. Zhao, W. Gong, M. Chen, E. Li, H. Wang, W. Xu, and S. Han, “Ghost imaging lidar via sparsity constraints,” Appl. Phys. Lett. 101, 141123 (2012). [CrossRef]
  16. M. Chen, E. Li, W. Gong, Z. Bo, X. Xu, C. Zhao, and S. Han, “Ghost imaging lidar via sparsity constraints in real atmosphere,” Opt. Photon. J. 3, 83–85 (2013). [CrossRef]
  17. S. Xia, B. Yan-Feng, Q. Tao, and S. Han, “Experimental investigation of quality of lensless ghost imaging with pseudo-thermal light,” Chin. Phys. Lett. 25, 3968–3971 (2008). [CrossRef]
  18. D. Donoho and V. Stodden, “Breakdown point of model selection when the number of variables exceeds the number of observations,” in International Joint Conference on Neural Networks, 2006 (IEEE, 2006), pp. 1916–1921.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited