Study of Gaussian mixture model for image wavelet coefficients
图像小波系数的高斯混合模型研究

A pixel-adaptive Gaussian mixture model was proposed in which each coefficient was a mixture of two normal distributions with the same zero mean value and different variance. Wavelet coefficients were classified into two categories using local Bayesian threshold, and the model parameters such as large and small variances, related probabilities, could be estimated from the information of the two classified coefficients in a neighbourlng window. The model was applied to image denoising, and Wiener filter was designed according to Bayesian posterior mean estimation theory. Comparative simulation results with three representative denoising algorithms demonstrate that this model-based filtering algorithm is effective.