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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 16, Iss. 2 — Jan. 21, 2008
  • pp: 975–992

A unified iterative denoising algorithm based on natural image statistical models: derivation and examples

Shan Tan and Licheng Jiao  »View Author Affiliations


Optics Express, Vol. 16, Issue 2, pp. 975-992 (2008)
http://dx.doi.org/10.1364/OE.16.000975


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Abstract

Lots of prior models for natural image wavelet coefficients have been proposed in the last two decades. Although most of them belong to the Scale Mixture of Gaussian (GSM) models, they are of obviously different analytical forms. As a result, Bayesian image denoising algorithms based on these prior models are also very different from each other. In this paper, we develop a novel image denoising algorithm by combining the Expectation Maximization (EM) scheme and the properties of the GSM models. The developed algorithm is of a simple iterative form and can converge quickly. It only uses the derivative information of a probability density function and is suitable for all GSM-type prior models that have an analytical probability density function. The developed algorithm can be viewed as a unified Bayesian image denoising framework. As examples, several classical and recently-proposed prior models for natural image wavelet coefficients are tested and some new results are obtained.

© 2008 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(110.7410) Imaging systems : Wavelets

ToC Category:
Image Processing

History
Original Manuscript: October 10, 2007
Revised Manuscript: November 21, 2007
Manuscript Accepted: November 23, 2007
Published: January 11, 2008

Citation
Shan Tan and Licheng Jiao, "A unified iterative denoising algorithm based on natural image statistical models: derivation and examples," Opt. Express 16, 975-992 (2008)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-16-2-975


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