基于复合激励模型的Surfacelet域多聚焦图像融合方法

Multi-focus Image Fusion Algorithm Based on Composite Incentive Model in Surfacelet Domain

针对基于传统多尺度分析对图像分解得到的方向子带数量较少,抑制噪声能力弱,融合图像边缘连贯性不好的缺点,本文提出一种基于Surfacelet变换和复合激励模型的多聚焦图像融合方法。通过分别将两幅图像经Surfacelet变换后得到若干不同频带子图像,该方法根据低频子带和高频子带的特点,建立复合激励模型,即分别把改进的拉普拉斯能量和与空间频率作为复合型PCNN的外部激励,采用复合型PCNN优选融合系数,改善融合效果。获取的融合图像的灰度级分布更加分散,图像纹理连贯,细节突出。实验结果表明,该算法克服传统多聚焦图像融合方法的缺陷,客观评价指标显示本方法优于Laplace、DWT和PCA等传统图像融合方法。

According to the ability of limited decomposition directional subband and difficult to suppress noise based on the traditional multi-scale analysis,a multi-focus image fusion method based on Surfacelet transform and composite incentive model is proposed.Original images are decomposed by Surfacelet transform to obtain a number of different frequency band sub-images.A composite incentive model is built based on the characteristics of the low frequency sub-band and high-frequency sub-band coefficients,namely improved-sum-modified-Laplacian and spatial frequency are selected as external stimulus of compound PCNN.Fusion coefficients are preferred by compound PCNN and the results are improved.The experimental results show that grayscale distribution of the fusion image is more dispersed and coherent image texture details are outstanding.The algorithm overcomes the traditional multi-focus image fusion defects, and the objective evaluation indexes show that this method is superior to that of Laplace,Discrete Wavelet Transform（DWT） and PCA traditional image fusion methods.