Progressive Face Super-Resolution with Non-Parametric Facial Prior Enhancement

Abstract

The main challenge of face super-resolution is to overcome facial distortions in an upscaling process. Recent works have utilized facial priors such as facial landmarks and component maps to generate a precise super-resolved image. However, the facial priors are estimated from the ground-truth and deep neural networks. Thus, recent works based on the facial priors are not only limited to specific datasets including the ground-truth, but also need sub-networks to extract facial priors. To solve these problems, we propose a progressive face super-resolution network with non-parametric facial prior enhancement, called as NPFNet, which extracts and highlights facial components without any tricks, such as the ground-truth and deep neural networks. The self-enhancement module facilitates our network to fully utilize facial distinct features to enhance super-resolved images with a parameter-free operation. Extensive experiments on CelebA and VGGFace2 demonstrate that the proposed method outperforms state-of-the-art face super-resolution methods in terms of visual quality and quantitative measurements.