An Efficient Locally Adaptive Wavelet Denoising Method Based on Bayesian MAP Estimation

An efficient locally adaptive wavelet denoising method is proposed by exploiting the correlation among image wavelet coefficients in a sub-band. Firstly, under the rule of Bayesian maximum a posteriori (MAP), we investigate Laplacian prior distribution based MAP estimator formula and sub-band adaptive MapShrink threshold. In order to make this threshold locally adaptive, a new stochastic model for wavelet coefficients is presented, in which each coefficient in a sub-band is assumed to be Laplacian with different marginal standard deviation, and these marginal standard deviations are modeled as random variables with high local correlation and thus can be estimated from a local neighborhood. Experiment results demonstrate the effectiveness of the presented algorithm, compared with the state of the art wavelet based image denoising methods