提出一种新的贝叶斯图像去噪方法,该方法以正态反高斯(NIG)模型为先验模型,对图像小波系数的稀疏分布统计建模,并用最大后验概率(MAP)估计法对小波系数进行估计。为了改善贝叶斯图像去噪的效果,还根据尺度间相关性的大小对小波系数分类进行处理。此外,还引入了递归循环平移(Cycle Spinning)算法对小波变换缺乏平移不变性产生的吉布斯现象进行抑制。实验结果表明该去噪算法能有效地去除图像中的高斯白噪声,更好地保留图像细节,提高图像的峰值信噪比值。

A new image denoising method based on Bayesian estimation is proposed. Normal inverse Guassian (NIG) model is used to describe the distributions of the wavelet coefficients of image,and Bayesian maximum a posteriori probability (MAP) estimator is used to estimate the noisy wavelet coefficients. In order to improve the behavior of Bayesian estimation,wavelet coefficients with different correlation are calculated with different ways. What′s more,Cycle Spinning algorithm is used to modify the Gibbs phenomenon which is caused by wavelet transform without translation invariance. The experimental results prove that this new method can remove Gaussian white noise effectively,reserve the edges better and improve the peak signal-to-noise ratio of the image.