关键词: 图像融合, Contourlet变换, 隐马尔可夫树模型, 混合高斯模型

Abstract: Contourlet-domain hidden Markov tree model can reflect the coefficients’ relevance of different scales and directions，a image fusion algorithm based on it is proposed.Source images are processed by Contourlet transform，low frequency and high frequency subband coefficients are obtained.The high frequency subband coefficients are modeled using hidden Markov tree and the model is trained using EM algorithm to get the posterior probability of the coefficients.Using the posterior probability to guide the fusion rules’ design of high frequency subband coefficients，it fuses the coefficients differently according to edge or background region.The fusion coefficients are inversed to get the final fusion result.Multi-focus images are taken to do fusing experiment，and the results are evaluated by joint entropy，entropy，correlation coefficient and clarity.The experiment shows that the proposed algorithm is better than traditional fusion algorithms based on Contourlet and wavelet-
domain hidden Markov tree model.

Key words: image fusion, Contourlet transform, hidden Markov tree model, Gaussian mixture model