Denoising of multicomponent images using wavelet least-squares estimators

  • Authors:

  • Steve De Backer;Aleksandra Piurica;Bruno Huysmans;Wilfried Philips;Paul Scheunders

  • Affiliations:

  • Visionlab, Department of Physics, Antwerp University, Universiteitsplein 1, B-2610 Wilrijk, Belgium;Department for Telecommunications and Information Processing (Telin), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department for Telecommunications and Information Processing (Telin), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Department for Telecommunications and Information Processing (Telin), Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Gent, Belgium;Visionlab, Department of Physics, Antwerp University, Universiteitsplein 1, B-2610 Wilrijk, Belgium

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2008

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Abstract

In this paper, we study denoising of multicomponent images. The presented procedures are spatial wavelet-based denoising techniques, based on Bayesian least-squares optimization procedures, using prior models for the wavelet coefficients that account for the correlations between the spectral bands. We analyze three mixture priors: Gaussian scale mixture models, Bernoulli-Gaussian mixture models and Laplacian mixture models. These three prior models are studied within the same framework of least-squares optimization. The presented procedures are compared to Gaussian prior model and single-band denoising procedures. We analyze the suppression of non-correlated as well as correlated white Gaussian noise on multispectral and hyperspectral remote sensing data and Rician distributed noise on multiple images of within-modality magnetic resonance data. It is shown that a superior denoising performance is obtained when (a) the interband covariances are fully accounted for and (b) prior models are used that better approximate the marginal distributions of the wavelet coefficients.