statistical wavelet-based image denoising using scale mixture of normal distributions with adaptive parameter estimation

Removing noise from images is a challenging problem in digital image processing. in this paper, we present an image denoising method based on a maximum a posteriori (map) density function estimator, which is implemented in the wavelet domain due to its energy compaction property. performance of the map estimator depends on the proposed model for noise-free wavelet coefficients. thus in the wavelet-based image denoising, selecting a proper model for wavelet coefficients is very important. in this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of the normal distribution family. the parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes so the intra-scale dependency of wavelet coefficients is also considered. the denoising results obtained confirm the effectiveness of the proposed method.