非下采样Contourlet变换的图像融合及评价*

分析和研究了非下采样Contourlet图像表示方法及其在图像变换中的优点，提出一种基于非下采样Contourlet变换的图像融合方法。首先将待融合源图像分解成不同尺度、多方向的频带；然后采取不同的融合方法对分解的高低频分量进行融合处理，低频系数采取局部能量优先的加权法融合，高频系数则采取局部梯度优先的加权法融合；最后将融合的各频带进行逆非下采样Contourlet变换得到融合图像。实验表明，在几种不同的客观评价标准下，该方法优于传统的小波域中的融合效果，能有效消除小波变换带来的光谱扭曲和假边缘现象。

Image fusion based on nonsubsampled Contourlet transform and evaluation

This paper introduced the characteristics of nonsubsampled Contourlet transform (NSCT), as well as its advantages in image transformation. NSCT was built upon nonsubsampled pyramids and nonsubsampled directional filter banks and it was a flexible multiscale, directional multiresolution and shift-unvariant image transform.Proposed a novel image fusion algorithm based on NSCT. Firstly, decomposed each source image in highpass subbands and lowpass subbands by NSCT. Secondly,used different fusion rules in different subbands, where local gradient preferential image fusion rule was adopted in highpass subbands and local energy preferential image fusion rule was adopted in lowpass subbandds. Finally, reconstructed the fused image by taking inverse transform of NSCT. The experimental results show that the performance of the novel algorithm is better than the traditional fusion algorithms based on wavelet transformation, and especially, the novel fusion algorithm can effectively eliminate the spectrum warping and the artificial edges caused by wavelet transform.