关键词: 位置块,稀疏表示,K近邻稀疏编码均值,人脸超分辨率

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