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Focusing on the issue that remote sensing fusion image has less spectrum information and low spatial resolution, combining with the characteristics of Curvelet and 2DPCA algorithm, a new image fusion algorithm combining the merits of the two algorithms will be proposed. First of all, perform inverse 2DPCA transform on the multispectral image (MS) to obtain the optimal projection axis U and the eigenvectors Q, then project each band of MS image onto X according to the projection rule to get each principal component.Second, project the Pan image matched histogram with MS image onto Q to acquire the 1st and other principal components, then apply Curvelet transform on the 1st principal components and Yk to obtain the corresponding coefficients.Third, Fuse the coefficients of the decomposed images with proper rules to get the new coefficients. Then perform inverse Curvelet transform on them to acquire the fused sub-image; Finally, perform inverse 2DPCA transform on the fused sub-image and the other components, to get the fused image; The new algorithm has prominent advantage in original image’s spectral information maintenance as well as in improving spatial detail. Experiments carried out on multispectral image and panchromatic high resolution image show that the new algorithm is better than that of former algorithms in comprehensive assessment.