Multivariate quasi-Laplacian mixture models for wavelet-based image denoising

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we introduce a class of multivariate quasi-Laplacian models as a generalization of the single-variable Laplacian distribution to multi-dimensions. A mixture model is used as the wavelet coefficient prior for the wavelet-based Bayesian image denoising algorithm. As a multivariate probability model, it is able to capture the intra-scale or inter-scale dependencies among wavelet coefficients. Two special cases are studied for orthogonal transform based image denoising. Efficient parameter estimation methods and denoising rules are derived for the two cases. Denoising results are compared with existing techniques in both PSNR values and visual qualities.

Original languageEnglish (US)
Title of host publication2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings
Pages2625-2628
Number of pages4
DOIs
StatePublished - Dec 1 2006
Event2006 IEEE International Conference on Image Processing, ICIP 2006 - Atlanta, GA, United States
Duration: Oct 8 2006Oct 11 2006

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2006 IEEE International Conference on Image Processing, ICIP 2006
CountryUnited States
CityAtlanta, GA
Period10/8/0610/11/06

Keywords

  • Image restoration
  • Wavelet transforms

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Shi, F., & Selesnick, I. W. (2006). Multivariate quasi-Laplacian mixture models for wavelet-based image denoising. In 2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings (pp. 2625-2628). [4107107] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2006.313048