This paper proposes a SVC (support vector clustering) based fusion rule according to unsupervised learning strategy. By employing the rule in multifocus image fusion applications,it solves the problems of region overlapping and abrupt transition brought about by the SVM (support vector machine) based fusion rule. The quality of the fused image is further enhanced. The undecimated discrete wavelet transform is applied to source images for multiresolution decomposition. Image feature data is extracted by means of grid,and it is then fed into the SVC algorithm which will generate distinct clusters. These resultant clusters are further processed by the domain discrimination algorithm and eventually distributed to two separate domains defined as complementary domain and redundant domain,in which choose-max method and weighted average method are used respectively to produce multiresolution representation of the fused image. Finally,the fused image is reconstructed by performing the corresponding inverse wavelet transform. The relation between the parameter q of SVC algorithm and the parameter RMSE used to evaluate the fused image is studied in detail. It is indicated by theoretical analysis and experimental results that SVC is appropriate for image fusion. Moreover,comparative studies show that the proposed SVC based fusion rule outperforms the existing SVM based ones.