基于变系数双变量模型的双变量阈值去噪法

Bivariate shrinkage denoising method based on variable parameter bivariate model

针对不同子带小波系数的父子小波系数联合分布不同的特点,提出可描述这一特征的父子小波系数的变系数双变量分布模型,并使用贝叶斯最大后验估计理论推导出基于新模型的双变量阈值函数。结合具有平移不变性和多方向选择性的双树复小波变换以进一步提高图像的去噪效果。实验结果表明,与Sendur的双变量阈值法相比,新方法去噪后图像的PSNR值有了提高,图像的主观视觉效果也得到改善。

In order to improve the denoising results of the bivariate shrinkage method,a new variable parameter bivariate model was proposed for the joint coefficient-parent distribution of wavelet coefficients,because the joint coefficient-parent distribution is different for coefficients in different scales and subbands.Based on the new model,a subband adaptive denoising method was proposed using Bayesian maximum a posteriori estimation theory.In the experiments,the dual tree complex wavelet transform of shift-invariance and directional selectivity was used for both the new method and bivariate shrinkage method.The results show that the Peak Signal-to-Noise Ratio（PSNR） values of the new method are improved.