Face Super Resolution based on Contrastive Learning

Abstract

Face super resolution (FSR) is a sub-field of super resolution (SR), which is to reconstruct low resolution (LR) face image into high resolution (HR) face image. Recently, the FSR methods based on face prior have been proved to be effective in FSR on higher upscaling factors. However, existing prior guided methods mostly adopt supervised prior extraction models trained with labels. The performance of supervised prior extraction method mainly depends on the accuracy of label so that the implicit informations of data are not fully utilized. And in practical application, the label acquisition work is routine and laborious. Therefore, to solve these problems, this paper proposes a novel contrastive learning (CL) based FSR method, which is based on the iterative collaboration of image reconstruction network and contrastive learning network. In each iteration, the reconstruction network uses the priors generated by the contrastive learning network to assist the image reconstruction and generates higher-quality SR images. Then, the SR image will feed into contrastive learning network to obtain more accurate prior. In addition, a new contrastive learning constraint function is designed to extract the representation of the augmented facial image as a prior by analysing the principal component information of the image. Quantitative and qualitative experimental results show that the proposed method is superior to the most advanced FSR method in high-quality face images super resolution reconstruction.