  Generative Adversarial Networks (GAN) have been employed for face super
resolution but they bring distorted facial details easily and still have
weakness on recovering realistic texture. To further improve the performance of
GAN based models on super-resolving face images, we propose PCA-SRGAN which
pays attention to the cumulative discrimination in the orthogonal projection
space spanned by PCA projection matrix of face data. By feeding the principal
component projections ranging from structure to details into the discriminator,
the discrimination difficulty will be greatly alleviated and the generator can
be enhanced to reconstruct clearer contour and finer texture, helpful to
achieve the high perception and low distortion eventually. This incremental
orthogonal projection discrimination has ensured a precise optimization
procedure from coarse to fine and avoids the dependence on the perceptual
regularization. We conduct experiments on CelebA and FFHQ face datasets. The
qualitative visual effect and quantitative evaluation have demonstrated the
overwhelming performance of our model over related works.
