中文摘要：

Tetrolet变换与目前广为采用的小波变换相比，在处理高维信号时具有更好的方向性，能够精确地表达图像的结构及纹理特征。本文将Tetrolet变换用于不同频谱图像的融合，以期获取更大的信息量。首先，将待融合的图像分别进行Tetrolet变换，得到不同尺度的高通和低通子带。然后，对低通子带采用基于局部区域梯度信息的融合方法得到低通融合系数，而对高通子带采用基于邻域方差加权的融合方法得到高通融合系数；最后，通过重构得到融合图像。采用多种图像进行了融合实验，其结果均表明，经Tetrolet变换获取的融合图像特征更为丰富、信息量更大，融合图像的信息熵和标准差都优于目前广为采用的小波变换和PCA变换图像融合算法；本文方法可有效地提高ATR系统和视觉对目标的识别探测概率和降低虚警率。

英文摘要：

Compared with wavelet transform which is widely used in image processing,Tetrolet transform has a better directionality of the structure and can express texture features of image precisely in dealing with high-dimensional signal. This paper introduces Tetrolet transform into different spectrum images for fusion to obtain a greater amount of information. Firstly, the Tetrolet transform was performed on the images which are fused to obtain high-pass and low-pass subbands on different scales and in various di- rections. Then,a method based on local region gradient information was applied to low-pass subbands to get the low-pass fusion coefficients, and a method based on neighboring region variance weighted-average was applied to high-pass subbands to get the high-pass fusion coefficients. Finally, the inverse Tetrolet transform was utilized to obtain fused image. Using a variety of images to perform fusion experiment, all the results have shown that the fused image has more abundant features and more amount of information by using Tetrolet transform. The entropy and standard deviation of the fusion image are better than those of the fused results by wavelet transform and PCA methods. The proposed method can effectively improve target detection probability and reduce the false alarm rate of the ATR and visual identification systems.