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中文摘要：
系数阈值是流行的去噪方法,其中阈值方式与大小的选择是一个重要的技术问题。该文依据非下采样Contourlet分解系数与其父系数之间的相关性,给出非高斯双变量分布,对该模型应用Bayes估值理论推导得到相应的非线性双变量阈值函数,综合非下采样Contourlet分解和双变量阈值函数,提出一种基于双变量阈值的非下采样Contourlet变换图像去噪方法（NSCTBI）。对于被加性高斯白噪声污染的图像,实验中将NSCTBI方法与非下采样Contourlet变换、小波域双变量阈值去噪等方法进行了比较,结果表明在大多数情况下,NSCTBI的PSNR结果相比这些方法高出0.5至2.3dB,在边缘特征方面保持了良好的视觉效果。
英文摘要：
As the main prevailing denoising method, how the threshold function works and what＇s the threshold value are the greatest importance techniques. Consider the dependencies between the coefficients and their parents, a non-Gaussian bivariate distribution is given, and corresponding nonlinear threshold function is derived from the model using Bayesian estimation theory. According to non-subsampled Contourlet transform and bivariate threshold function, a novel Non-Subsampled Contourlet Transform based on Bivariate threshold function （NSCTBI） for image denoising is proposed. This scheme achieves enhanced estimation results for images that are corrupted with additive Gaussian noise over a wide range of noise variance. To evaluate the performance of the proposed algorithms, the results are compared with existent algorithms, such as non-subsampled Contourlet transform and wavelet-based bivariate threshold function method for image denoising. The simulation results indicate that the proposed method outperforms the others 0.5-2.3dB in PSNR, and keep better visual result in edges information reservation as well.
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