MAP estimation of Pearson Type IV random vectors in AWGN

Abstract

This paper is concerned with wavelet-based image denoising using Bayesian technique. In conventional denoising process, The parameters of probability density function (PDF) are usually calculated from the first few moments, mean and variance. In this work, a new image denoising algorithm based on Pearson Type IV random vectors is proposed. Pearson Type IV is used because it allows higher-order moments (skewness and kurtosis) to be incorporated into the noiseless wavelet coefficients' probabilistic model. One of the cruxes of the Bayesian image denoising methods is to estimate statistical parameters for a shrinkage function. We employ maximum a posterior (MAP) estimation to calculate local variances with Gamma density prior for local observed variances and Gaussian distribution for noisy wavelet coefficients. The experimental results show that the proposed method yields good denoising results.