To improve the quality of the fused image, we propose a remote sensing image fusion method based on sparse representation. In the method, first, the source images are divided into patches and each patch is represented with sparse coefficients using an overcomplete dictionary. Second, the larger value of sparse coefficients of panchromatic (Pan) image is set to 0. Third, Then the coefficients of panchromatic (Pan) and multispectral (MS) image are combined with the linear weighted averaging fusion rule. Finally, the fused image is reconstructed from the combined sparse coefficients and the dictionary. The proposed method is compared with intensity-hue-saturation (IHS), Brovey transform (Brovey), discrete wavelet transform (DWT), principal component analysis (PCA) and fast discrete curvelet transform (FDCT) methods on several pairs of multifocus images. The experimental results demonstrate that the proposed approach performs better in both subjective and objective qualities.