关键词: 双变量模型, 双变量阈值, 双树复小波变换, 贝叶斯估计, 图像去噪

Abstract: 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.

Key words: Bivariate Model(BM), Bivariate Shrinkage(Bivshrink), Dual Tree Complex Wabelet Transform（DTCWT）, bayesian estimation, image denoising