Abstract: A novel sparse representation based super-resolution(SR) method was proposed to reconstruct a high resolution(HR) face image from a low resolution(LR) observation via training samples．First,a specific LR and HR over-complete dictionary pair was learned for a certain patch over the patches in all training samples with the same position．Second,K Nearest Neighbor(KNN) sparse coding mean constrain was used to make the sparse representation of the input patch more accurate．Third,the HR patch was hallucinated via the sparse representation coefficients and the HR dictionary．At last,we formed the final HR face image by integrating the hallucinated HR patches together．Experiments validate the proposed method in extensive data．Compared to some state-of-the-art methods,our method exhibits better performance both in subjective and objective quality．

Key words: Position-patch,Sparse representation,K nearest neighbor(KNN) sparse coding mean,Face hallucination