一种新的结合SVM和FNN的多聚焦图像融合算法

A New Multi-focus Image Fusion Algorithm with Combination of SVM and FNN

针对基于分块的图像融合中分块裂痕和实际融合特征的不确定等问题,提出一种结合支持向量机(SVM)和模糊神经网络(FNN)的多聚焦图像融合新方法。首先,通过模糊C均值聚类(FCM)和SVM获得FNN的网络参数,利用构建的模糊神经网络,将分割的图像块分成清晰区域、模糊区域和过渡区域三类;然后用模糊神经网络的反模糊化输出作为权值因子对三类区域进行加权融合,输出融合的多聚焦图像。最后,通过均方根误差、平均绝对误差和峰值信噪比等指标对多种融合算法进行融合质量评价。实验结果表明,提出的融合算法鲁棒性和计算性能较好,基本满足实际图像融合的需求,且融合质量评价也表明本文方法优于现有的融合算法。

To deal with the problems of cracks among blocks and the uncertainty of real characteristics of image fusion based on block,this paper proposed a new multi-focus image fusion method by combining support vector machine（SVM）wits fuzzy neural network（FNN）.Firstly,FCM and SVM were used to obtain the parameters of FNN and the block was divided into clear,blurring and transitional zones based on the FNN.Then the three classified areas were merged with weighting to get the fused multi-focus images,where the weight factors were obtained as the defuzzication outputs of the fuzzy neural network.Finally,the qualities of various fusion algorithm were evaluated by the root mean square error（RMSE）,the mean absolute error（MAE）and peak signal to noise ratio（PSNR）.The experimental results show that the proposed fusion algorithm has good robustness and computing performance,which basically meets the demand of practical image fusion,and the fusion quality evaluations illustrate that our method has an advantage over the existing fusion algorithm.