基于小波域局部统计模型的图像去噪方法

提出了一种基于小波域局部统计模型图像去噪方法.该方法利用图像小波子带的方向性特点以及小波系数尺度内和尺度间的相关性:将小波系数尺度内的相关性建模为一种各向异性马尔可夫随机场(Markov Random Field,MRF)先验概率模型,将小波系数尺度间的相关性建模为局部奇异性的条件概率模型.通过在贝叶斯框架中采用这种先验概率模型和条件概率模型可以得到一种具有自适应性的贝叶斯萎缩函数.利用这种萎缩函数可以实现对小波系数的修正.实验结果表明利用该方法进行图像去噪能够取得良好的效果,同时可以有效地保留图像的细节.

Image denoising based on local statistical models in wavelet domain

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.