基于非下采样Contourlet变换和双变量模型的图像去噪

Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model

在研究了非下采样Contourlet变换(NSCT)和贝叶斯最大后验估计理论框架下的双变量模型的基础上,该文将二者结合起来,提出了一种新的图像去噪算法。算法在利用变换平移不变性和多方向选择性优点的同时,充分挖掘了图像NSCT系数尺度内和尺度间的双重相关性,并详细阐述了噪声估计方法。仿真结果和分析表明,与当前一些典型的去噪算法相比,该文算法的客观评价指标PSNR和去噪后图像的主观视觉效果都有明显的提高和改善,有效地保持了原图像中的细节和纹理信息。

This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform（NSCT） and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NSCT＇s advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scaie correlations of NSCT coefficients, and elaborates the method of noise estimation. Compared with some current outstanding denoising methods, the simulation results and analysis show that the proposed algorithm obviously outperforms in both Peak Signal-to-Noise Ratio（PSNR） and visual quality, and effectively preserves detail and texture information of original images.