To improve the efficiency of the polarimetric image fusion methods via NMF, a fast fusion method based on sparse NMF was proposed. Firstly, the polarization parameter images were acquired by computing from intensity images of different polarization angle. The original data set was organized by the polarization parameter images. Secondly, the sparse constraint was added to NMF and the cost function was solved by online dictionary learning algorithm of sparse representation. Then, the data set was factorized by sparse NMF and three feature basis images sorted by definition and variance were obtained. Next, after histogram matching, these three sorted feature basis images were mapped into three color channels of HSI color model. Finally, the fused image was achieved by transforming the image from HSI to RGB color model. Compared with the method based on NMF, the running time is improved with 120 times. One fusing process can be finished in 1.5 s. Experiment results show that the proposed method not only has good fusion results but also enhances the running efficiency evidently.