基于Contourlet域隐马尔可夫树模型的图像融合算法

本文提出一种基于Contourlet域隐马尔可夫树(HMT)模型的图像融合算法。由于Contourlet变换能克服小波变换在处理高维信号时的不足，它比小波变换具有更好的方向性、较高的逼近精度和更好的稀疏表达性能。而HMT模型能有效捕获尺度间、尺度内的contourlet系数特性。因此将Contourlet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，更好的提取图像边缘特征，为融合提取更多的特征信息。实验结果表明本文的算法获得的融合图像视觉效果良好，是一种有效且可行的融合算法。

Image Fusion Algorithm Based on Contourlet Domain Hidden Markov Tree Models

A novel image fusion method based on Contourlet domain hidden Markov tree models is proposed.Contourlet transform provides a flexible multiresolution,local and directional image expansion,and also a sparse representation for two-dimensional piecewise smooth signals building images.Contourlet HMT can capture all inter-scale,inter-direction,and inter-location dependencies of the Contourlet coefficients.Aiming at the different frequency bands of Contourlet decomposition with different characteristics,different fusion rules are applied to different subbands.In the low-frequency information,the weighted average mean is used to obtain the fused low-frequency information.Contourlet HMT is applied to design low-frequency information rule,the fusion method has the ability to strengthen the relationship among the Contourlet coefficients,extract more detailed and exact information from the original images.The fused images by the proposed algorithm exhibit good performance both in subjective and objective standards.Experimental results also show the simplicity and effectiveness of the method and its advantages over the conventional approaches.