Adaptive Wiener denoising using a Gaussian scale mixture model in the wavelet domain

J. Portilla, V. Strela, M. J. Wainwright, E. P. Simoncelli

Research output: Contribution to conferencePaperpeer-review

Abstract

We describe a statistical model for images decomposed in an overcomplete wavelet pyramid. Each coefficient of the pyramid is modeled as the product of two independent random variables: an element of a Gaussian random field, and a hidden multiplier with a marginal log-normal prior. The latter modulates the local variance of the coefficients. We assume subband coefficients are contaminated with additive Gaussian noise of known covariance, and compute a MAP estimate of each multiplier variable based on observation of a local neighborhood of coefficients. Conditioned on this multiplier, we then estimate the subband coefficients with a local Wiener estimator. Unlike previous approaches, we (a) empirically motivate our choice for the prior on the multiplier; (b) use the full covariance of signal and noise in the estimation; (c) include adjacent scales in the conditioning neighborhood. To our knowledge, the results are the best in the literature, both visually and in terms of squared error.

Original languageEnglish (US)
Pages37-40
Number of pages4
StatePublished - 2001
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001

Other

OtherIEEE International Conference on Image Processing (ICIP)
CountryGreece
CityThessaloniki
Period10/7/0110/10/01

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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