基于正态逆高斯模型的非下采样Contourlet变换图像去噪

摘要

提出一种基于正态逆高斯先验模型的非下采样Contourlet变换图像去噪算法.在非下采样Contourlet变换域中,以正态逆高斯模型为先验模型,对图像分解系数的稀疏分布统计建模,估计每个子带内的模型参数,在贝叶斯最大后验概率估计准则下推导出与正态逆高斯模型相应的阈值函数表达式,以此对图像进行去噪处理.对于被加性高斯白噪声污染的图像,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,提高图像的峰值信噪比值,在边缘特征方面保持了良好的视觉效果.%A novel non-subsampled Contourlet transform denoising scheme based on the normal inverse Gaussian prior (NIG) and Bayesian estimation has been proposed. Normal inverse Gaussian model is used to describe the distributions of the image coefficients of each subband in non-subsampled Contourlet transform domain, corresponding threshold function is derived from the model using Bayesian maximum a posteriori probability estimation theory. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance.The simulation results indicate that the proposed method can remove Gaussian white noise effectively, improve the peak signal-to-noise ratio of the image, and keep better visual result in edges information reservation as well.