Image Denoising Based Bayesian Neural Network Prior Statistical Modeling
Xingming Long · Chongqing Shifan Daxue xuebao. Ziran kexue ban · 2009
Image processing based wavelet coefficients prior statistical models plays one of great improtant roles in modern image processing techniques.Owing to the defaults of fitting of Gaussian or Laplace functions,a Bayesian model of neural network(BMNN)to study the statistical dependency of wavelet coefficients is firstly presented.Secondly,its parameters are estimated by modern particle samplers(Monte Carlo) methods—Gibbs algorithm according to the characteristics of the suggested BMNN model.Then the relationship of wavelet coefficients is discussed in detail.Finally,a practical application of denoising image by using the BMNN model is demonstrated and the result shows that,on one hand the suggested method can express wavelet coefficients dependency efficiently,on the other,high quality visual effects and peak signal-to-noise ratio(PSNR) are achieved.