In order to eliminate the aliasing phenomena of the spectrum in each direction subband of the fusion algorithm in contourlet domain and to improve the accuracy of extracting effective coefficients, a multifocus image fusion method was proposed based on the Brenner function and the New Contourlet Transform with Sharp Frequency Localization (NCT-SFL). Firstly, the new contourlet transform was used to decompose the multifocus images which to be fused. And then the traditional arithmetic mean fusion rule was used to fuse the low-frequency coefficients and the maximum local energy based on the Brenner function to fuse the high-frequency coefficients. Finally, the inverse new contourlet transform was used to obtain fused images. Using the proposed fusion method, the experimental results demonstrate that the algorithm can effectively extract the contour information of the images which to be fused, and under the premise of obtaining a better performance in terms of subjective vision, objective evaluation criterions of mutual information and transferred edge information have improved by 99. 34% and 77.95% respectively. In addition, the more levels of the new contourlet decomposition, the more obvious advantages of the algorithm will show.%为了消除Contourlet融合算法中各方向子带的频谱混叠现象,同时提高有效系数提取的正确率,提出了一种基于Brenner函数与新轮廓波变换(NCT-SFL)的多聚焦图像融合算法.该算法先使用新轮廓波变换分解多聚焦待融合图像;然后对低频系数采用传统的算术平均融合规则,高频系数采用基于Brenner函数的局部能量最大的融合规则,最后经逆新轮廓波变换得到融合图像.实验结果表明:该算法能有效提取待融合图像的轮廓信息,并在获得较优的主观视觉前提下,客观评价指标互信息与转移的边缘信息分别提高了99.34％与77.95％.此外,新轮廓波分解层数较多时,该算法的优势更为明显.
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