MAP estimation of two-sided gamma random vectors in AWGN

In this work, we present new Bayesian estimator for spherically-contoured Two-Sided Gamma random vectors in additive white Gaussian noise (AWGN). This PDF is used in view of the fact that it is more peaked and the tails are heavier to be incorporated in the probabilistic modeling of the wavelet coefficients. 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.