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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 48, Iss. 12 — Apr. 20, 2009
  • pp: 2350–2355

Blind deconvolution of a noisy degraded image

Jianlin Zhang, Qiheng Zhang, and Guangming He  »View Author Affiliations

Applied Optics, Vol. 48, Issue 12, pp. 2350-2355 (2009)

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We develop a unified algorithm for performing blind deconvolution of a noisy degraded image. By incorporating a low-pass filter into the asymmetric multiplicative iterative algorithm and extending it to multiframe blind deconvolution, this algorithm accomplishes the blind deconvolution and noise removal concurrently. We report numerical experiments of applying the algorithm to the restoration of short-exposure atmosphere turbulence degraded images. These experiments evidently demonstrate that the unified algorithm has both good blind deconvolution performance and high-resolution image restoration.

© 2009 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
(100.1455) Image processing : Blind deconvolution

ToC Category:
Image Processing

Original Manuscript: November 19, 2008
Revised Manuscript: February 25, 2009
Manuscript Accepted: March 11, 2009
Published: April 15, 2009

Jianlin Zhang, Qiheng Zhang, and Guangming He, "Blind deconvolution of a noisy degraded image," Appl. Opt. 48, 2350-2355 (2009)

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  1. J. Hadamard, Lectures on Cauchy's Problem in Linear Partial Differential Equations (Yale Univ. Press, 1923).
  2. R. C. Puetter, T. R. Gosnell, and A. Yahil, “Digital image reconstruction: deblurring and denoising,” Annu. Rev. Astron. Astrophys. 43, 139-194 (2005). [CrossRef]
  3. S. Prasad, “Statistical-information-based performance criteria for Richardson-Lucy image deblurring,” J. Opt. Soc. Am. A 19, 1286-1296 (2002). [CrossRef]
  4. J.-A. Conhello, “Superresolution and convergence properties of the expectation-maximization algorithm for maximum-likelihood deconvolution of incoherent images,” J. Opt. Soc. Am. A 15, 2609-2619 (1998). [CrossRef]
  5. P. Campisi and K. Egiazarian, Blind Image Deconvolution: Theory and Applications (CRC Press, 2007). [CrossRef]
  6. L. Bar, N. Kiryati, and N. Sochen, “Image deblurring in the presence of impulse noise,” Int. J. Comput. Vis. 70, 279-298(2006). [CrossRef]
  7. J. Zhang, Q. Zhang, and G. He, “Blind image deconvolution by means of asymmetric multiplicative iterative algorithm,” J. Opt. Soc. Am. A 25, 710-717 (2008). [CrossRef]
  8. A. Papoulis, Probability, Random Variables, and Stochastic Processes (McGraw-Hill, 1991).
  9. A. van der Schaaf and J. H. van Hateren, “Modeling the power spectra of natural images: statistics and information,” Vision Res. 36, 2759-2770 (1996). [CrossRef] [PubMed]
  10. S. S. Young, R. G. Driggers, B. P. Teaney, and E. L. Jacobs, “Adaptive deblurring of noisy images,” Appl. Opt. 46, 744-752(2007). [CrossRef] [PubMed]
  11. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd ed. (Prentice-Hall, 2002).
  12. J. W. Goodman, Introduction to Fourier Optics, 2nd ed. (McGraw-Hill, 1996).
  13. S. M. Jefferies and J. C. Christou, “Restoration of astronomical images by iterative blind deconvolution,” Astrophys. J. 415, 862-874 (1993). [CrossRef]
  14. J. W. Goodman, Statistical Optics (Wiley, 2000).
  15. D. G. Sheppard, B. R. Hunt, and M. W. Marcellin, “Iterative multiframe superresolution algorithms for atmospheric-turbulence-degraded imagery,” J. Opt. Soc. Am. A 15, 978-992(1998). [CrossRef]
  16. M. C. Roggemann and B. Welsh, Imaging through Turbulence (CRC Press, 1996).
  17. J. Canny, “A computational approach for edge detection,” IEEE Trans. Pattern Anal. Machine Intell. pami-8, 679-698 (1986). [CrossRef]

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