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; applying a wavelet transform to the Pan image and the interpolated MS image to obtain a wavelet transformed Pan image and a wavelet transformed MS image; extracting features, in the form of vectors of low frequency coefficients from the wavelet transformed MS image with missing high frequency coefficients, and features in the form of vectors of low frequency coefficients and high frequency coefficients from the wavelet transformed Pan image without missing values; separating the features into features without missing values and features with missing values; learning a dictionary from the features without missing values, wherein the features are sparsely represented in the dictionary; predicting the values for the features with the missing values using the dictionary, wherein the predicting further comprises: minimizing an objective function 
 {circumflex over (X)} MS =argmin X,α ∥X−[ ( X MS L ) T ( X Pan H ) T ] T ∥ F 2 +λ∥X−Dα∥ F 2 s.t.∀l,∥α l ∥ 0 ≦T 1 .  (3) where X is a set of L′ features from an HR MS image, D is a dictionary and α=[α 1 , α 2 ,. .. , α L′ ] is a set of sparse coefficient vectors representing X in the dictionary D, T 1 is a desired sparsity, and ∥·∥ F denotes a Frobenius norm, λ is a non-zero weighting coefficient; combining, after the predicting, the features of the low frequency wavelet coefficients and the high frequency coefficients to form a fused wavelet coefficient map; and applying an inverse wavelet transform to the fused wavelet coefficient map to obtain a fused MS image, wherein the steps are performed in a processor.