基于Contourlet域HMT模型的图像融合

针对小波域隐马尔可夫树融合速度较慢、方向信息少的缺点，提出了一种图像融合新算法．首先将待融合图像进行Contourlet分解得到低频系数和高频方向系数，低频系数采用模极小值的规则融合，高频方向系数经过Contourlet域隐马尔可夫树训练后求得边缘概率密度函数，与原系数相乘得到新的方向系数，然后采用局部内积的规则对高频系数融合．对融合后的系数进行逆Contourlet变换，从而得到融合后的图像．由于Contourlet域隐马尔可夫树模型在细尺度方面能够跨越几个相邻的方向子带，方向子带间具有与尺度相似的统计模式，因此该算法不仅能提高建模精度，而且能降低运算的复杂度(减少了参数数目)．仿真实验表明，相比Contourlet和小波域同类算法，这种算法能够得到更加清晰光滑的融合图像;标准差、平均梯度、平均交叉熵等统计指标均比小波域隐马尔可夫树有显著改善，训练速度提高了17倍左右，且能够快速融合采用小波域隐马尔可夫树算法比较困难的图像．

Image fusion algorithm using the Contourlet HMT model

To overcome the limitation of the wavelet domain hidden Markov tree(HMT) in computational speed and sparse direction,a novel image fusion algorithm using the contourlet HMT model is presented.After the Contourlet transform on the images,the low-frequency subbands are compared to preserve the coefficients whose module are minimum,and local inner-product are performed on the new high-frequency directional subbands which are acquired from the product by the high-frequency directional coefficients and the edge ...