Abstract
A novel method based Full Bayesian Model for Neural Network (FBMNN) to study the statistical dependency of wavelet coefficients is presented. To overcome the ignorance of the relationship between wavelet coefficients, we introduce the FBMNN to model joint probability density distribution (JPDF) of Child and Parent wavelet coefficients. According to the characteristics of the suggested FBMNN-JPDF model, its parameters are estimated by reversible jump MCMC (rjMCMC) algorithm. Finally, a practical application on denoising image by using the FBMNN-JPDF model is demonstrated and the result shows that the suggested method can express wavelet coefficients dependency efficiently.KeywordsHide Markov ModelMixture Gaussian ModelWavelet CoefficientStatistical DependencyNature ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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