  Face Super-Resolution (SR) is a subfield of the SR domain that specifically
targets the reconstruction of face images. The main challenge of face SR is to
restore essential facial features without distortion. We propose a novel face
SR method that generates photo-realistic 8x super-resolved face images with
fully retained facial details. To that end, we adopt a progressive training
method, which allows stable training by splitting the network into successive
steps, each producing output with a progressively higher resolution. We also
propose a novel facial attention loss and apply it at each step to focus on
restoring facial attributes in greater details by multiplying the pixel
difference and heatmap values. Lastly, we propose a compressed version of the
state-of-the-art face alignment network (FAN) for landmark heatmap extraction.
With the proposed FAN, we can extract the heatmaps suitable for face SR and
also reduce the overall training time. Experimental results verify that our
method outperforms state-of-the-art methods in both qualitative and
quantitative measurements, especially in perceptual quality.
