OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 41, Iss. 32 — Nov. 11, 2002
  • pp: 6884–6889

Noniterative blind data restoration by use of an extracted filter function

James N. Caron, Nader M. Namazi, and Chris J. Rollins  »View Author Affiliations


Applied Optics, Vol. 41, Issue 32, pp. 6884-6889 (2002)
http://dx.doi.org/10.1364/AO.41.006884


View Full Text Article

Acrobat PDF (1933 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A signal-processing algorithm has been developed where a filter function is extracted from degraded data through mathematical operations. The filter function can then be used to restore much of the degraded content of the data through use of a deconvolution algorithm. This process can be performed without prior knowledge of the detection system, a technique known as blind deconvolution. The extraction process, designated self-deconvolving data reconstruction algorithm, has been used successfully to restore digitized photographs, digitized acoustic waveforms, and other forms of data. The process is non-iterative, computationally efficient, and requires little user input. Implementation is straightforward, allowing inclusion into many types of signal-processing software and hardware. The novelty of the invention is the application of a power law and smoothing function to the degraded data in frequency space. Two methods for determining the value of the power law are discussed. The first method assumes the power law is frequency dependent. The function derived comparing the frequency spectrum of the degraded data with the spectrum of a signal with the desired frequency response. The second method assumes this function is a constant of frequency. This approach requires little knowledge of the original data or the degradation.

© 2002 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(100.2000) Image processing : Digital image processing
(100.3020) Image processing : Image reconstruction-restoration

Citation
James N. Caron, Nader M. Namazi, and Chris J. Rollins, "Noniterative blind data restoration by use of an extracted filter function," Appl. Opt. 41, 6884-6889 (2002)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-41-32-6884


Sort:  Author  |  Year  |  Journal  |  Reset

References

  1. J. N. Caron, N. M. Namazi, R. L. Lucke, C. J. Rollins, and P. R. Lynn, Jr., “Blind data restoration with an extracted filter function,” Opt. Lett. 26, 1164–1166 (2001).
  2. J. N. Caron, U.S. Patent pending, anticipated acceptance date: August, 2003.
  3. A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).
  4. M. I. Sezan, “Survey of recent developments in digital image restoration,” Opt. Eng. 29, 393–404 (1990).
  5. B. Jähne, Digital Image Processing (Springer-Verlag, Berlin, 1997).
  6. P. M. Clarkson and H. Stark, eds., Signal Processing Methods for Audio, Images, and Telecommunications (Academic, San Diego, Calif., 1995).
  7. J. G. Proakis and D. G. Manolakis, Introduction to Signal Processing (Macmillan, New York, 1988).
  8. N. F. Law and D. T. Nguyen, “Multiple frame projection based blind deconvolution,” Electron. Lett. 31, 1733–1734 (1995).
  9. S. Barraza-Felix and B. R. Frieden, “Regularization of the image division approach to blind deconvolution,” Appl. Opt. 38, 2232–2239 (1999).
  10. O. Shalvi and E. Weinstein, “Super-exponential methods for blind deconvolution,” IEEE Trans. Info. Theory 39, 504–519 (1993).
  11. B. L. Satherly and P. J. Bones, “Zero tracks for blind deconvolution of blurred ensembles,” Appl. Opt. 33, 2197–2205 (1994).
  12. G. R. Ayers and J. C. Dainty, “Iterative blind deconvolution method and its applications,” Opt. Lett. 13, 547–549 (1988).
  13. Y. Yitzhaky, R. Milberg, S. Yohaev, and N. S. Kopeika, “Comparison of direct blind deconvolution methods for motion-blurred images,” Appl. Opt. 38, 4325–4332 (1999).
  14. G. Arfken, “Mathematical Methods for Physicists,” Academic, San Diego, Calif., 1985.
  15. N. Wiener, The Extrapolation, Interpolation, and Smoothing of Stationary Time Series with Engineering Applications (Wiley, New York, 1949).
  16. C. W. Helstrom, “Image restoration by the method of least-squares,” J. Opt. Soc. Am. 57, 297–303 (1967).
  17. D. Slepian, “Linear least-squares filtering of distorted images,” J. Opt. Soc. Am. 57, 918–922 (1998).
  18. Y. Yitzhaky, I. Mor, A. Lantzman, and N. S. Kopeika, “Direct method for restoration of motion-blurred images,” J. Opt. Soc. Am. A 15, 1512–1519 (1998).
  19. Image courtesy of National Aeronautics and Space Administration/Jet Propulsion Laboratory/California Institute of Technology.
  20. Image courtesy of the Arizona Board of Regents and the Center for Image Processing in Education, Tucson, Ariz.
  21. J. N. Caron, Y. Yang, J. B. Mehl, and K. V. Steiner, “Gas-coupled laser acoustic detection for ultrasound inspection of composite materials,” Mater. Eval. 58, 667–671 (2000).

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