  Most of the face hallucination methods are designed for complete inputs. They
will not work well if the inputs are very tiny or contaminated by large
occlusion. Inspired by this fact, we propose an obscured face hallucination
network(OFHNet). The OFHNet consists of four parts: an inpainting network, an
upsampling network, a discriminative network, and a fixed facial landmark
detection network. The inpainting network restores the low-resolution(LR)
obscured face images. The following upsampling network is to upsample the
output of inpainting network. In order to ensure the generated
high-resolution(HR) face images more photo-realistic, we utilize the
discriminative network and the facial landmark detection network to better the
result of upsampling network. In addition, we present a semantic structure
loss, which makes the generated HR face images more pleasing. Extensive
experiments show that our framework can restore the appealing HR face images
from 1/4 missing area LR face images with a challenging scaling factor of 8x.
