  To make the best use of the underlying structure of faces, the collective
information through face datasets and the intermediate estimates during the
upsampling process, here we introduce a fully convolutional multi-stage neural
network for 4$\times$ super-resolution for face images. We implicitly impose
facial component-wise attention maps using a segmentation network to allow our
network to focus on face-inherent patterns. Each stage of our network is
composed of a stem layer, a residual backbone, and spatial upsampling layers.
We recurrently apply stages to reconstruct an intermediate image, and then
reuse its space-to-depth converted versions to bootstrap and enhance image
quality progressively. Our experiments show that our face super-resolution
method achieves quantitatively superior and perceptually pleasing results in
comparison to state of the art.
