Image Fusion Based on Multi-feature Fuzzy Clustering

In this method,the fuzzy C-means clustering algorithm is used to segment the image in the feature space formed by multiple features of training samples,and then a multi-scale wavelet decomposition is performed on each region.Second,the weighting factors are constructed based on the local energy and the fuzzy similarity measure defined by Cauchy function.The wavelet coefficients of the fused image are acquired by the weighting factors.Finally,the fused image is obtained by taking the inverse wavelet transform.The performance of the image fusion method is evaluated using five criteria including root mean square error,peek-to-peek signa-l to-noise ratio,entropy,cross entropy and mutual information.The evaluation results validate the proposed image fusion method.