为了同时保证融合质量和效率，提出了一种基于多尺度高斯滤波和形态学变换的图像融合方法。设计了多尺度高斯滤波，将源图像分解为一系列细节图像和近似图像，并使用多尺度顶帽和底帽分解来完全提取近似图像中不同尺度的明暗细节。构造了多尺度形态学内外边缘分解，以充分提取细节图像的边缘信息。实验结果表明，该方法与典型的基于多尺度分解的融合方法相当甚至更好，同时比一些先进的基于多尺度分解的方法（如NSCT和NSST）运算速度快得多。

To ensure the fusion quality and efficiency simultaneously， a novel image fusion method based on multi-scale Gaussian filtering and morphological transform is proposed. The multi-scale Gaussian filtering is designed to decompose the source images into a series of detail images and approximation images. The multi-scale top- and bottom-hat decompositions are used respectively to fully extract the bright and dark details of different scales in each approximation image. The multi-scale morphological inner- and outer-boundary decompositions are constructed to fully extract boundary information in each detail image. Experimental results demonstrate that the proposed method is comparable to or even better in comparison with typical multi-scale decomposition-based fusion methods. Additionally， the method operates much faster than some advanced multi-scale decomposition-based methods like NSCT and NSST.