A Face Image Super-Resolution Algorithm Based on Global Reconstruction and Position-Patch Residue Compensation

Focusing on the high loss of subtle facial details in traditional algorithm,a face image super-resolution algorithm through global reconstruction and position-patch based residue compensation is presented. The optimal coefficients of the low-resolution training images are computed and transformed into high-resolution space to reconstruct the global high-resolution image. The residue training set is obtained by a smoothing and down-sampled processing. The residue compensation based on position is performed to better recover face subtle details using the residue training set. Experimental results show that the proposed approach synthesizes high-resolution faces with more details and the average of peak signal-to-noise ratios is improved about 0.65 dB to 3.55 dB compared with some existing learning-based methods.