An adaptive fusion method of multisensor images based on nonsubsampled contourlet transform is proposed in this paper, which can select the fusion weights of the low-frequency co-efficients adaptively via golden section algorithm. The nonsubsampled contourlet transform is a flexible multi-scale, multi-direction and shift-invariant image decomposition, which is suitable for representing images bearing abundant detail and directional information. This is employed for fu-sing the directional high-frequency coefficients. For the directional high-frequency coefficients, the higher adding level of the directional subbands is used to select the better coefficient for fu-sion. The nonsubsampled contourlet transform can also avoids introducing ringing artifacts to fused images compared to ordinary method. Experimental results show that the proposed method achieves better fusion efficiency compared to image fusion methods based on Laplacian pyramid trans-form, wavelet transform, stationary wavelet transform and contourlet transform respectively.%提出了一种基于非下采样Contourlet变换的多传感器图像自适应融合方法,采用黄金分割法搜索最优的低频融合权值.自适应地对多传感器图像的低频子带系数进行融合.非下采样Contourlet变换是一种新的图像多尺度、多方向的表示方法,适合表达具有丰富细节信息及方向信息的图像,且该变换具有平移不变性,可以避免一般方法对融合图像引入的振铃效应,它的高频方向子带捕获了多传感器图像的显著特征,文中采用同一尺度下方向子带信息和取大的规则对高频系数进行融合.实验结果表明,与基于拉普拉斯塔、小波、平稳小波和Contourlet变换的方法比较,文中所提出的方法可以获得较好的融合效果.
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