Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Bayesian Wavelet Threshold Approach for Image Denoising

  • Ahn, Yun-Kee (Department of Applied Statistics, Yonsei University) ;
  • Park, Il-Su (Department of Applied Mathematics, Yosu National University) ;
  • Rhee, Sung-Suk (Department of Business Administration, Seowon University)
  • Published : 2001.04.01
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Abstract

Wavelet coefficients are known to have decorrelating properties, since wavelet is orthonormal transformation. but empirically, those wavelet coefficients of images, like edges, are not statistically independent. Jansen and Bultheel(1999) developed the empirical Bayes approach to improve the classical threshold algorithm using local characterization in Markov random field. They consider the clustering of significant wavelet coefficients with uniform distribution. In this paper, we developed wavelet thresholding algorithm using Laplacian distribution which is more realistic model.

Keywords

References

  1. Journal of the American Statistical Association v.90 Adapting to unknown smoothness via wavelet shrinkage Donoho,D.;Johnstone
  2. Tech. report, Katholieke Universiteit Leuven Empirical Bayes approach to improve wavelet thresholding for image noise reduction Jansen,M.;Bultheel,A.
  3. Journal of Chemical Physics v.21 Equation of state calculations by fast computing machines Metropolis,N.(et al.)
  4. Bayesian Inference in Wavelet Based Models v.141 Bayesian Denoising of Visual Images in the Wavelet Domain Simoncelli,E.P.;P,Muller(ed.);B.Vidakovix(ed.)