Generative Adversarial Networks (GANs) 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 diiculty will be greatly
alleviated and the generator can be enhanced to reconstruct clearer
contour and iner 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 ine and avoids the dependence on the perceptual
regularization. We conduct experiments on CelebA and FFHQ face
datasets. The qualitative visual efect and quantitative evaluation
have demonstrated the overwhelming performance of our model
over related works.