OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 2, Iss. 11 — Nov. 26, 2007

Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry

Chen Tang, Wenjing Lu, Song Chen, Zhen Zhang, Botao Li, Wenping Wang, and Lin Han  »View Author Affiliations


Applied Optics, Vol. 46, Issue 30, pp. 7475-7484 (2007)
http://dx.doi.org/10.1364/AO.46.007475


View Full Text Article

Enhanced HTML    Acrobat PDF (2085 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.

© 2007 Optical Society of America

OCIS Codes
(070.6110) Fourier optics and signal processing : Spatial filtering
(100.5010) Image processing : Pattern recognition
(120.6160) Instrumentation, measurement, and metrology : Speckle interferometry

ToC Category:
Fourier Optics and Signal Processing

History
Original Manuscript: May 1, 2007
Revised Manuscript: August 22, 2007
Manuscript Accepted: August 28, 2007
Published: October 11, 2007

Virtual Issues
Vol. 2, Iss. 11 Virtual Journal for Biomedical Optics

Citation
Chen Tang, Wenjing Lu, Song Chen, Zhen Zhang, Botao Li, Wenping Wang, and Lin Han, "Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry," Appl. Opt. 46, 7475-7484 (2007)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-46-30-7475


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. C. Tang, F. Zhang, B. Li, and H. Yan, "Performance evaluation of partial differential equation models in electronic speckle pattern interferometry and δ-mollification method of phase map," Appl. Opt. 45, 7392-7400 (2006). [CrossRef] [PubMed]
  2. A. P. Witkin, "Scale-space filtering," in the International Joint Conference on Artificial Intelligence (Karlsruhe, 1983), pp. 1019-1022.
  3. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. Mach. Intell. 12, 629-639 (1990). [CrossRef]
  4. F. Catté, P.-L. Lions, J.-M. Morel, and T. Coll, "Image selective smoothing and edge detection by nonlinear diffusion," SIAM (Soc. Ind. Appl. Math.) J. Numer. Anal. 29, 182-193 (1992).
  5. L. Alvarez, P.-L. Lions, and J.-M. Morel, "Image selective smoothing and edge detection by nonlinear diffusion," SIAM (Soc. Ind. Appl. Math.) J. Numer. Anal. 29, 845-866 (1992).
  6. G. Sapiro and V. Caselles, "Contrast enhancement via image evolution flows," Graph. Models Image Process. 59, 407-416 (1997). [CrossRef]
  7. C. Tang, F. Zhang, and Z. Chen, "Contrast enhancement for electronic speckle pattern interferometry fringes by the differential equation enhancement method," Appl. Opt. 45, 2287-2294 (2006). [CrossRef] [PubMed]
  8. D. Mumford and J. Shah, "Optimal approximations by piecewise smooth functions and associated variational problems," Commun. Pure Appl. Math. 17, 577-685 (1989). [CrossRef]
  9. J. M. Morel and S. Solimini, Variational Methods in Image Segmentation (Birkhaäuser, 1995).
  10. Y. Chen, C. A. Z. Barcelos, and B. A. Mairz, "Smoothing and edge detection by time-varying coupled nonlinear diffusion equations," Comput. Vision Image Understand. 82, 85-100 (2001). [CrossRef]
  11. C. Tang, F. Zhang, H. Yan, and Z. Chen, "Denoising in electronic speckle pattern interferometry fringes by the filtering method based on partial differential equations," Opt. Commun. 260, 91-96 (2006). [CrossRef]
  12. "Neural Networks," http://www.statsoft.com/textbook/stneunet.html.
  13. J. Principe, N. Euliano, and W. Lefebvre, Neural and Adaptive System--Fundamentals Through Simulations (Wiley, 2000).
  14. J. Nazari and O. K. Ersoy, "Implementation of back-propagation neural networks with MatLab," http://docs.lib.purdue.edu/ecetr/275.
  15. N. A. Ochoa, F. M. Santoyo, A. J. Moore, and C. P. López, "Contrast enhancement of electronic speckle pattern interferometry addition fringes," Appl. Opt. 36, 2783-2787 (1997). [CrossRef] [PubMed]

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