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Optics Letters

| RAPID, SHORT PUBLICATIONS ON THE LATEST IN OPTICAL DISCOVERIES

  • Editor: Anthony J. Campilo
  • Vol. 32, Iss. 17 — Sep. 1, 2007
  • pp: 2583–2585

Multishrinkage: analytical form for a Bayesian wavelet estimator based on the multivariate Laplacian model

Shan Tan and Licheng Jiao  »View Author Affiliations


Optics Letters, Vol. 32, Issue 17, pp. 2583-2585 (2007)
http://dx.doi.org/10.1364/OL.32.002583


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Abstract

We develop a novel multivariate Bayesian wavelet estimator of a simple analytical form that is computationally effective for the image denoising problem. The estimator is derived from the multivariate Laplacian model by using the maximum a posteriori rule. We find the multivariate estimator produces restoration results of high quality, both visually and in terms of peak signal-to-noise ratio.

© 2007 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.7410) Image processing : Wavelets

ToC Category:
Image Processing

History
Original Manuscript: April 30, 2007
Revised Manuscript: July 10, 2007
Manuscript Accepted: July 26, 2007
Published: August 22, 2007

Citation
Shan Tan and Licheng Jiao, "Multishrinkage: analytical form for a Bayesian wavelet estimator based on the multivariate Laplacian model," Opt. Lett. 32, 2583-2585 (2007)
http://www.opticsinfobase.org/ol/abstract.cfm?URI=ol-32-17-2583


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References

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