Many earth observing satellites not only take ordinary red-green-blue (RGB) images but further include bands from the near-infrared and infrared spectrum and provide so called multispectral images. The additional bands greatly help in classication and identication tasks, but the drawback of the additional spectral information is that each spectral band has rather low spatial resolution. Therefore, many multispectral satellites such as Quickbird or the Landsat7 satellite include a panchromatic image at high spatial resolution. Pan-sharpening is the process of fusing a low resolution multispectral image with a high resolution panchromatic image to obtain a high resolution multispectral image. We propose a new pan-sharpening method called Variational Wavelet Pan-sharpening (VWP) that combines wavelet fusion and alligning the isocontours of each multipectral band with the panchromatic image as an energy minimization problem. Furthermore, we introduce additional energy terms to explicitly preserve the color information within each band and the correlation between bands. In this talk we present the VWP model, show numerical results and extend the model to sharpening hyperspectral images (with up to 210 bands) with the help of screenshots from Google Maps.