Pan-sharpening is a common post processing operation for captured multispectral satellite imagery, where the spatial resolution of images gathered in various spectral bands is enhanced by fusing them with a panchromatic image captured at a higher resolution. In this paper, pan-sharpening is formulated as the problem of jointly estimating the high-resolution (HR) multispectral images to minimize an objective function comprised of the sum of squared residual errors in physically motivated observation models of the low-resolution (LR) multispectral and the HR panchromatic images and a correlation-dependent regularization term. Two pan-sharpening methods based on the non-subsampled contourlet transform (NSCT) are proposed. NSCT is very efficient in representing the directional information and capturing intrinsic geometrical structures of the objects. It has characteristics of high resolution, shift-invariance, and high directionality. In proposed we use the NSCT transformation in both Multispectral image and Pan image and then reconstruct the image and get the high quality pan-sharp image.