1. A method for Pan-sharpening a single panchromatic (Pan) image and a single multispectral (MS) image, comprising: interpolating the MS image to a resolution of the Pan image to produce an interpolated MS image; extracting features, in the form of vectors, from the Pan image and the interpolated MS image; decomposing the features into features without missing values and features with missing values, wherein the missing values in the Pan image are spectral values and the missing values in the interpolated MS image are interpolated spatial values; learning a dictionary from the features without missing values; predicting the values for the features with the missing values using the dictionary, wherein the predicting minimizes an objective function 
 {circumflex over ( X )}=argmin X,α ∥X−{tilde over (D)}α∥ F 2 +λ∥E MS X− X MS ∥ F 2 s.t.∀l,∥α l ∥ 0 ≦T 0 and E Pan X=X Pan where X is a set of L′ vectors representing the Pan and interpolated MS images, {tilde over (D)} is the dictionary, α=[α 1 , α 2 ,. .. , α L ′] is a set of sparse coefficient vectors representing X in the dictionary {tilde over (D)}, T 0 is a desired sparsity, and ∥·∥ F denotes a Frobenius norm, λ is a weighting vector, E MS and E Pan are first and last rows of identity matrices; and merging the interpolated MS image with the Pan image including the predicted values into a merged image, wherein the steps are performed in a processor.