Face hallucination via K-selection mean constrained sparse representation

In this paper, a novel sparse representation based super-resolution (SR) method is 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 is learned for a certain patch over the patches in all training samples with the same position. Second, K-selection mean constrain is used to make the sparse representation of the input patch more accurate. Third, the HR patch is hallucinated via the sparse representation coefficients and the HR dictionary. At last, we form 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.