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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 12 — Jun. 7, 2010
  • pp: 12872–12889

High order statistics based blind deconvolution of bi-level images with unknown intensity values

Jeongtae Kim and Soohyun Jang  »View Author Affiliations

Optics Express, Vol. 18, Issue 12, pp. 12872-12889 (2010)

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We propose a novel linear blind deconvolution method for bi-level images. The proposed method seeks an optimal point spread function and two parameters that maximize a high order statistics based objective function. Unlike existing minimum entropy deconvolution and least squares minimization methods, the proposed method requires neither unrealistic assumption that the pixel values of a bi-level image are independently identically distributed samples of a random variable nor tuning of regularization parameters. We demonstrate the effectiveness of the proposed method in simulations and experiments.

© 2010 Optical Society of America

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(100.1455) Image processing : Blind deconvolution

ToC Category:
Image Processing

Original Manuscript: March 1, 2010
Revised Manuscript: April 23, 2010
Manuscript Accepted: May 25, 2010
Published: June 1, 2010

Jeongtae Kim and Soohyun Jang, "High order statistics based blind deconvolution of bi-level images with unknown intensity values," Opt. Express 18, 12872-12889 (2010)

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  1. T. J. Holmes, "Blind deconvolution of quantum-limited incorehent imagery:maximum-likelihood approach," J. Opt. Soc. Am. A 9, 1052-1061 (1992). [CrossRef] [PubMed]
  2. D. A. Fish, A. M. Brinicombe, E. R. Pike and G. Walker, "Blind deconvolution by means of the Richardson-Lucy algorithm," J. Opt. Soc. Am. A 12, 58-65 (1995). [CrossRef]
  3. S. Esedoglu, "Blind deconvolution of bar code signals," Inverse Probl. 20, 121-135 (2004). [CrossRef]
  4. E. Y. Lam, "Blind bi-level image restoration with iterated quadratic programming," IEEE Trans. Circ. Syst. Part 2 52, 52-56 (2007). [CrossRef]
  5. J. Kim and H. Lee, "Joint nonuniform illumination estimation and deblurring for bar code signals," Opt. Express 17, 14817-14837 (2007). [CrossRef]
  6. D. Kundur and D. Hatzinakos, "A novel blind deconvolution scheme for image restoration using recursive filtering," IEEE Trans. Signal Process. 45, 375-390 (1998). [CrossRef]
  7. G. R. Ayers, and J. C. Dainty, ‘Iterative blind deconvolution method and its application," Opt. Lett. 13, 547-549 (1998). [CrossRef]
  8. T. Li and K. Lii, "A joint estimation approach for two-tone image deblurring by blind deconvolution," IEEE Trans. Image Process. 11, 847-858 (2002). [CrossRef]
  9. H. Wu, "Minimum entropy deconvolution for restoration of blurred two-tone images," Electronics Letters 26, 1183-1184 (1990). [CrossRef]
  10. N. Miura, N. Baba, S. Isobe, M. Noguchi, and Y. Norimoto, "Binary star reconstruction with use of the blind deconvolution method," J. Mod. Opt. 39, 1137-1146 (1992). [CrossRef]
  11. D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Trans. Image Process. 2, 223-235 (1993).
  12. J. A. Cadzow, "Blind deconvolution via cumulant extrema," IEEE Signal Processing Magazine., 24-41 (1996). [CrossRef]
  13. P. Campisi and K. Egiazarian, eds., Blind image deconvolution: Theory and applications, (CRC, New York, 2007). [CrossRef]
  14. H. Lee and J. Kim, "Retrospective correction of nonuniform illumination on bi-level images," Opt. Express 15, 23880-23893 (2009). [CrossRef]
  15. Y. Shen, E. Y. Lam, and N. Wong, "Binary image restoration by positive semidefinite programming," Opt. Lett. 32, 121-123 (2007). [CrossRef]
  16. M. D. Sacchi, D. R. Velis, and A. H. Comingues, "Minimum entropy deconvolution with frequency-domain constraints," Geophysics 59, 938-945 (1994). [CrossRef]
  17. D. Donoho, "On minimum entropy deconvolution," Applied Time Series Analysis II, D. F. Findley ed., (Academic, New York, 1991).
  18. N. F. Law and R. G. Lane, "Blind deconvolution using least squares minimisation," Opt. Commun. 128, 341-352 (1996). [CrossRef]
  19. J. Kim, "Restoration of bi-level images via iterative semi-blind Wiener filtering," Trans. KIEE 57, 1290-1294 (2008).
  20. H. L. Van Trees, Detection, estimation, and modulation theory, Part 1 (Wiley, 1968).
  21. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using MATLAB, (Prentice Hall, New York, 2002).
  22. T. Mathworks, Optimization toolbox user’s guide (Mathworks Inc., 2003).
  23. T. Chan and C. K. Wong, "Total variation blind deconvolution," IEEE Trans. Image Process. 7, 370-375 (1998). [CrossRef]
  24. E. K. P. Chong and S. H.  Zak, An introduction to optimization, 3rd ed., (Wiley-Interscience, New Jersey, 2008).
  25. W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, Numerical recipes in C++, 2nd ed., (Cambridge, 2005).

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