  Previous research on face restoration often focused on repairing a specific
type of low-quality facial images such as low-resolution (LR) or occluded
facial images. However, in the real world, both the above-mentioned forms of
image degradation often coexist. Therefore, it is important to design a model
that can repair LR occluded images simultaneously. This paper proposes a
multi-scale feature graph generative adversarial network (MFG-GAN) to implement
the face restoration of images in which both degradation modes coexist, and
also to repair images with a single type of degradation. Based on the GAN, the
MFG-GAN integrates the graph convolution and feature pyramid network to restore
occluded low-resolution face images to non-occluded high-resolution face
images. The MFG-GAN uses a set of customized losses to ensure that high-quality
images are generated. In addition, we designed the network in an end-to-end
format. Experimental results on the public-domain CelebA and Helen databases
show that the proposed approach outperforms state-of-the-art methods in
performing face super-resolution (up to 4x or 8x) and face completion
simultaneously. Cross-database testing also revealed that the proposed approach
has good generalizability.
