复小波包变换域混合统计模型图像降噪算法

Image denoising algorithm using mixed statistical model in complex wavelet packet transform

中文关键词  图像去噪  四树复小波包变换  层间相关性  非高斯双变量模型  零均值高斯分布模型

英文关键词  image denoising  quad-tree complex wavelet packet transform  inter-scale correlation  non-Gaussian bivariate mode  zero mean Gaussian distributing model

中文摘要

      该方法利用四树复小波包变换具有的移不变性、良好的方向选择性和对高频信号的细致分析能力等特点, 把含噪图像分解成低频逼近子图和若干高频方向子图; 在保留低频逼近子图复系数不变的同时, 利用复系数层间相关性的强弱把高频方向子图分为主要类和次要类. 对主要类和次要类复系数分别进一步采用非高斯双变量模型和零均值高斯分布模型进行噪声抑制. 实验结果表明, 无论是峰值信噪比(PSNR)指标, 还是在视觉效果上, 本文方 法的去噪性能均好于传统的双树复小波变换去噪、四树复小波包变换去噪和小波域高斯尺度混合模型去噪, 在有效抑制噪声的同时, 具有很好的图像边缘和细节保护能力.

英文摘要

      The noisy image is decomposed into low frequency approximate subimages and high frequency directional subimages by using the quad-tree complex wavelet packet transform(QCWPT) which has the advantages of shift-invariance, high directional resolution and fine discrimination of high frequency signals. The complex coefficients in low frequency approximate subimages are kept unchanged, while the high frequency directional subimages are categorized as major type and minor type according to their inter-scale correlation. Noises in both types are removed by using of the non-Gaussian bivariate model and the zero mean Gaussian distributing model, respectively. In comparing either the power signal-tonoise ratio(PSNR) index or the visual effects with other methods, the presented scheme outperforms the traditional dualtree complex wavelet transform, QCWPT and wavelet domain Gaussian scale mixtures. Experiments also show that the presented scheme achieves an excellent balance between the suppression of noises and the preservation of image details and edge.