基于贝叶斯估计的小波阈值图像降噪方法

METHOD OF WAVELET THRESHOLD DENOISING BASED ON BAYESIAN ESTIMATION

提出一种新的基于贝叶斯估计的小波收缩阈值的图像降噪方法 ,该方法是通过最小Bayes风险的方法对图像小波变换后的小波系数进行估计 ,这种对小波系数的估计不仅与子带的方向和层次有关 ,而且与小波系数的大小有关 .试验结果表明该方法比一般小波收缩阈值方法的降噪效果要好 ;还表明在峰值信噪比较低时该方法的降噪效果比Wiener滤波差 。

A new image denoising method of wavelet shrinkage threshold based on Bayesian estimation was proposed. The coefficients of wavelet transform of image were estimated by minimizing Bayes risk in the proposed method. The estimation is not only related to the orientation and the level of the subband, but also to the wavelet coefficients. The experimental results show that the denoising effect of the proposed method is better than that of other methods based on wavelet shrinkage. Although the denoising effect of this method is worse than one of Wiener filters at low peak-signal-noise ratio, it is better than Wiener filters at high peak-signal-noise ratio.