Image denoising based on local statistical models in wavelet domain
Ke Wang · Guangdian gongcheng · 2007
An image denoising method was presented based on local statistical models in wavelet domain.This method was adaptive to the wavelet subbands corresponding to three orientations in the image and took into account inter-scale and intra-scale dependencies between wavelet coefficients.An anisotropic Markov Random Field(MRF) model was used to represent prior knowledge about the intra-scale dependencies between the wavelet coefficients.The inter-scale dependencies between the wavelet coefficients were measured from the local singularity,which appeared as a conditional model.Based on these models in a Bayesian framework,an adaptive Bayesian shrinkage function was obtained and each modified coefficient was decided separately.Experimental results demonstrate that this method improves the denoising performance and preserves the details of the image.