A Minorization-Maximization Algorithm for Maximum a Posteriori Signal Estimation

We develop an iterative algorithm based on minorization - maximization optimization to determine the maximum a posteriori estimate of the signal. We focus on linear Gaussian signal model with a family of heavy-tailed prior distributions which can be represented as scale mixture of Gaussian. We then modify the proposed algorithm for wavelet domain image denoising. Experimental results show that using complex wavelet representations, the performance of the proposed algorithm is very competitive with that of the state-of-the-art algorithms.