A Super-Resolution Method for Face Images Using Position-Dictionary Pairs

A novel method using position-dictionary pairs is proposed to reconstruct a high resolution frontal face image from a single low resolution version.Since the contents and structures in the same position of face image patches are similar and are more likely to be better represented by the combination of the same dictionary atoms,face images used for training are blocked into overlapped position-patches.Then a small position-dictionary pair is trained for each position.A basic high resolution image can be recovered for each low resolution input by using these position-dictionary pairs.Finally,the visual quality of the reconstructed image is improved by using a residual compensation method.Experimental results show that face images reconstructed by the proposed method have better visual effect.A comparison with the algorithm of image super-resolution via raw image patches based sparse representation show that the SSIM of the proposed method is 0.082 higher;while the training time is shortened about 5 times.