基于图像金字塔的分维融合算法

数据融合是进行目标检测和识别的重要技术之一，但是在融合过程中不可避免地会产生信息损失。如何保留尽可能多的有用信息，关键在于融合算法的选取。由于图像金字塔中不同级的图像之间具有自相似性，而自相似性又是分形分维的基础。于是，根据图像金字塔的特点，提出了一种新的图像融合方法——基于图像金字塔的分维融合算法，将不同源图像分别分解成不同尺度的金字塔图像序列，在相应的尺度上以分形分维作为权系数进行融合，并采用中波红外和长波红外图像作为源图像进行融合实验，实验结果表明该方法是可行的。

Fractal dimension fusion method based on image pyramid

Data fusion is an important technique to detect and recognize target from images. However, loss of information is unavoidable in fusion process. It is crucial to select fusion algorithm and avoid the loss of useful information. Because images in different levels of image pyramid were self-similar, which was the foundation of fractal, a new image fusion algorithm was presented based on image pyramid and fractal dimension. Different source images were decomposed into image pyramid sequence with different scales. Corresponding level images were merged, with fractal dimension as weight. An experiment was given using a mid-wave and a long-wave infrared image as two source images. Experiment results show the algorithm is feasible.