Novel Nonparametric Bayesian Estimator for Image Denoising in Wavelet Domain
Shengqian Wang · Journal of Chinese Computer Systems · 2008
A novel nonparametric Bayesian estimator for image denoising in wavelet domain is presented.In this approach,normal inverse Gaussian(NIG)distribution is used as a prior model to capture the sparseness of the wavelet expansion.Compared with other distributions,such as the generalized Gaussian distribution(GGD),α-stable models,and Bessel K forms(BKF),it fits very well to the distributions of wavelet coefficients of natural images.Based on L2 based Bayes rules,a posterior conditional means estimator is designed.Finally,the estimator is used to image denoising.Experimental results show that compared with several recently published algorithms,the proposed method achieves state-of-art performance in terms of peak signal-to-noise ratio and visual effect.