基于IE 和AG 的小波图像融合方法

为客观评价几种小波融合图像算法的优劣, 采用了基于信息熵(IE: Information Entropy)和平均梯度(AG: Average Grads)的融合图像质量评价方法。首先选取了两组不同的源图像, 其中一组为曝光度不同的两幅图像, 另一组是焦距不同的两幅图像。然后对图像进行4 层小波分解, 低频系数全部采用像素点平均值法进行融合, 高频系数分别运用区域绝对值最大法、像素点绝对值最大法、区域最大梯度法以及像素点平均值法4 种小波系数融合规则进行小波图像融合, 最后分别比较了4 种融合图像的IE 和AG 值。实验结果表明, 将4 种小波融合方法进行比较, 基于像素点的最大绝对值法IE 和AG 值最大。对于曝光度不同的两幅图像, 其IE为7. 206 5 bit, AG 为5. 983 7 10^-5 , 对于焦距不同的两幅图像, 其IE 为7. 130 8 bit, AG 为4. 794 3 10^-5 。可见, 4 种融合规则相比较, 基于像素点的最大绝对值法融合效果最好, 从而验证了该方法的有效性。

Wavelet Fusion Method Based on IE and AG

In order to measure the performance of several wavelet-based image fusion methods, an image fusion assessment method is used. Two types of source image are used to perform the experiments, four-level wavelet decomposition based method is used to assess the images. Low frequency coefficient of wavelet decomposition matrix is used to fuse images by pixel average value method. Low frequency coefficient of wavelet decomposition matrix is used to fuse images by pixel value method. Then the methods are studied by some experiments. The results show that the information entropy of maximum pixel value and average grads is greatest. The information entropy are 7. 206 5 bit and 5. 983 7*10^-5 bit for underexposure images, 7. 130 8 and 4. 794 3*10^-5 bit for out of focus images. The proposed method gives the best performance in the experiments.