改进的contourlet变换域隐马尔可夫树模型  

Improved contourlet-domain hidden Markov tree models

作　　者：梅雪[1] 李久贤[1] 夏良正[1] 

机构地区：[1]东南大学自动化学院

出　　处：《电路与系统学报》2008年第4期65-69,共5页Journal of Circuits and Systems

基　　金：国内科技合作和长三角联合攻关项目(2005E60007);江苏省"六大人才高峰"项目(07E24)

摘　　要：Contourlet域隐马尔科夫树(C_HMT)模型不但可以描述尺度间的相关性,而且可以对方向子带间contourlet系数的相关性做出统计描述,是一种比小波域HMT模型更为有效的系数相关性描述方法。本文提出了一种改进的contourlet变换域HMT模型,节点的状态不只依赖于其父节点的状态,而且兼顾到其父节点相邻节点的状态。这种模型可以进一步捕捉尺度间contourlet变换系数更为丰富的相关性,从而能够更准确和有效的刻画contourlet变换系数的非高斯性和持续性。将该模型应用于图像的去噪,并与另外几种典型的去噪算法作定性比较,验证了本文提出的改进的C_HMT模型在图像去噪性能方面有一定的优越性。Contourlet coefficients are modeled using a hidden Markov tree （HMT） model with Gaussian mixtures that can capture all interscale, interdirection, and interlocation dependencies, which is more valid than wavelet HMT model. Put forward an improved contourlet-domain hidden Markov tree model, where the state of the contourlet coefficients depend not only on the state of its parent node but on the state of the twin of its parent as well. This strategy can catch richer interscale correlation of the contourlet coefficient, and then is more suitable for representing non-Gaussian statistics and persistence of the contourlet coefficient. The improved model is used in image denoising and compared with the other denoising method, such as wavelet HMT and contourlet HMT, and the experiment results show the method is more advanced in estimating original image signal.

关 键 词：C_HMT CONTOURLET变换 图像去噪 隐马尔叮夫树模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]