Image Denoising with a Mixture of Gaussian Distributions with Local Parameters in Wavelet Domain

The proposed model for noise-free data distribution play an important role for maximum a posteriori (MAP) estimator. Thus, in the wavelet based image denoising, it is necessary to select a proper model for distribution of wavelet coefficients. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each subband with a mixture of Gaussian probability density functions (pdfs) that parameters of mixture model are local. The mixture pdf is able to model the long tailed property of wavelet coefficients and the local parameters can model the empirically observed correlation between the coefficient amplitudes. Therefore, the statistical properties of wavelet coefficients are better modelled by using this new pdf. Within this framework, we describe a new image denoising algorithm based on designing a MAP estimator, which use the mixture distributions with high local correlation. The simulation results show that our proposed technique achieves better performance than several published methods such as denoising based on mixture pdfs without local parameters both visually and in terms of peak signal-to-noise ratio (PSNR).