中文摘要：

传统的基于Contourlet变换的图像融合方法大都忽略了Contourlet系数之间的相关性，导致特征信息的丢失。本文根据隐马尔可夫树（HMT）模型的两种状态和3组概率确定能有效捕获尺度问、尺度内的Contourlet系数特性的似然概率，设计了图像融合规则。实验结果表明，Contour—let域HMT模型应用于图像融合领域，能充分挖掘数据之间的相关性，为融合图像提取更全面、准确的特征纹理信息。

英文摘要：

Traditional image fusion method based Contourlet transform always ignores the relationship of the Contourlet coefficients. This paper proposes a novel image fusion method based on Contourlet domain hidden Markov tree （HMT） model. The fusion method can strengthen the relationship among Contourlet coefficients and extract more detailed and exact information from the original images. Firstly,the original images are decomposed using Contourlet transform. Secondly, different frequency bands have different characteristics,so this paper designs the different fusion rules in different frequencies. The proposed method calculates likelihood function according to the parameters of Contourlet HMT. Finally the fused coefficients are reconstructed to obtain fusion results. Two sets of images are taken as experimental da- ta, and subjective and objective standards are used to evaluate the results. Experimental results have veri- fied the simplicity and effectiveness of the method. The results show that the proposed method can pre- serve much more infonnatiorL The proposed fusion rule based On likelihood function can extract much more and exact characteristics for fused images. And it is an effective and feasible algorithm.