Image denoising based on a mixture of bivariate Gaussian models in complex wavelet domain

H. Rabbani, M. Vafadoost, I. Selesnick, S. Gazor

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

Abstract

Recently, it has been shown that algorithms exploiting dependencies between coefficients for modeling probability density function (pdf) of wavelet coefficients, could achieve better results for image demising in wavelet domain compared with the ones based on the independence assumption. In this context, we design a bivariate maximum a posteriori (MAP) estimator which relies on a mixture of bivariate Gaussian models. This model not only is bivariate but also is mixture and therefore, using this new statistical model, we are able to better capture heavy-tailed natures of the data as well as the interscale dependencies of wavelet coefficients. The simulation results show that our proposed technique achieves better performance than several published methods both visually and in terms of peak signal-to-noise ratio (PSNR).

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006
Pages149-153
Number of pages5
DOIs
StatePublished - Dec 1 2006
Event2006 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006 - Cambridge, MA, United States
Duration: Sep 4 2006Sep 6 2006

Publication series

NameProceedings of the 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006

Other

Other2006 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006
CountryUnited States
CityCambridge, MA
Period9/4/069/6/06

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Keywords

  • Bivariate pdf
  • Complex wavelet transform
  • MAP estimator
  • Mixture model

ASJC Scopus subject areas

  • Mechanical Engineering
  • Materials Science(all)
  • Medicine(all)

Cite this

Rabbani, H., Vafadoost, M., Selesnick, I., & Gazor, S. (2006). Image denoising based on a mixture of bivariate Gaussian models in complex wavelet domain. In Proceedings of the 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006 (pp. 149-153). [4201290] (Proceedings of the 3rd IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors, ISSS-MDBS 2006). https://doi.org/10.1109/ISSMDBS.2006.360121