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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Vol. 17, Iss. 2 — Feb. 1, 2000
  • pp: 265–275

Image restoration with the Viterbi algorithm

Casey Miller, Bobby R. Hunt, Michael W. Marcellin, and Mark A. Neifeld  »View Author Affiliations

JOSA A, Vol. 17, Issue 2, pp. 265-275 (2000)

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The Viterbi algorithm (VA) is known to given an optimal solution to the problem of estimating one-dimensional sequences of discrete-valued pixels corrupted by finite-support blur and memoryless noise. A row-by-row estimation along with decision feedback and vector quantization is used to reduce the computational complexity of the VA and allow the estimation of two-dimensional images. This reduced-complexity VA (RCVA) is shown to produce near-optimal estimation of random binary images. In addition, simulated restorations of gray-scale images show the RCVA estimates to be an improvement over the estimates obtained by the conventional Wiener filter (WF). Unlike the WF, the RCVA is capable of superresolution and is adaptable for use in restoring data from signal-dependent Poisson noise corruption. Experimental restorations of random binary data gathered from an optical imaging system support the simulations and show that the RCVA estimate has fewer than one third of the errors of the WF estimate.

© 2000 Optical Society of America

OCIS Codes
(060.4510) Fiber optics and optical communications : Optical communications
(100.1830) Image processing : Deconvolution
(100.2000) Image processing : Digital image processing
(100.3020) Image processing : Image reconstruction-restoration
(100.6640) Image processing : Superresolution
(210.4680) Optical data storage : Optical memories

Original Manuscript: April 15, 1999
Revised Manuscript: October 15, 1999
Manuscript Accepted: October 15, 1999
Published: February 1, 2000

Casey Miller, Bobby R. Hunt, Michael W. Marcellin, and Mark A. Neifeld, "Image restoration with the Viterbi algorithm," J. Opt. Soc. Am. A 17, 265-275 (2000)

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  1. H. C. Andrews, B. R. Hunt, Digital Image Restoration (Prentice-Hall, Englewood Cliffs, N.J., 1977).
  2. A. K. Jain, Fundamentals of Digital Image Processing (Prentice-Hall, Englewood Cliffs, N.J., 1989).
  3. T. J. Holmes, “Maximum likelihood image restoration adapted for noncoherent optical imaging,” J. Opt. Soc. Am. A 5, 666–673 (1988). [CrossRef]
  4. G. Forney, “The Viterbi algorithm,” Proc. IEEE 61, 268–278 (1973). [CrossRef]
  5. G. Forney, “Maximum likelihood sequence estimation of digital sequences in the presence of intersymbol interference,” IEEE Trans. Inf. Theory IT-18, 363–378 (1972). [CrossRef]
  6. J. Heanue, K. Gürkan, L. Hesselink, “Signal detection for page-access optical memories with inter-symbol interference,” Appl. Opt. 35, 2431–2438 (1996). [CrossRef] [PubMed]
  7. K. M. Chugg, “Performance of optimal digital page detection in a two-dimensional ISI/AWGN channel,” presented at the Asilomar Conference on Signals, Systems and Computers, November 5, 1996, Pacific Grove, Calif.
  8. C. Miller, B. R. Hunt, M. A. Neifeld, M. W. Marcellin, “Binary image reconstruction via 2-D Viterbi search,” in Proceedings of the IEEE International Conference on Image Processing (Institute of Electrical and Electronics Engineers, New York, N.Y., 1997), pp. 181–184.
  9. J. G. Proakis, Digital Communications, 3rd ed. (McGraw Hill, New York, 1995).
  10. K. M. Chugg, X. P. Chen, M. A. Neifeld, “Two-dimensional equalization in coherent and incoherent page-oriented optical memory,” J. Opt. Soc. Am. A 16, 549–562 (1999). [CrossRef]
  11. H. Press, S. Teukolsky, W. Vetterling, B. Flannery, Numerical Recipes in C (Cambridge U. Press, New York, 1992).
  12. R. W. Gerchberg, “Super-resolution through error energy reduction,” Opt. Acta 21, 709–720 (1974). [CrossRef]
  13. A. Gersho, R. M. Gray, Vector Quantization and Signal Compression (Kluwer Academic, Boston, Mass., 1992).
  14. Y. Linde, A. Buzo, R. M. Gray, “An algorithm for vector quantization design,” IEEE Trans. Commun. COM-28, 84–95 (1980). [CrossRef]
  15. D. G. Sheppard, A. Bilgin, M. S. Nadar, B. R. Hunt, M. W. Marcellin, “A vector quantizer for image restoration,” IEEE Trans. Image Process. 7, 119–124 (1998). [CrossRef]
  16. L. R. Bahl, J. Cocke, F. Jelinek, J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inf. Theory IT-20, 284–287 (1974). [CrossRef]
  17. X. P. Chen, K. M. Chugg, “Near-optimal data detection for two-dimensional ISI/AWGN channels using concatenated modeling and iterative algorithms,” presented at the International Conference on Communications, June 7–11, Atlanta, Georgia.

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