  Most existing face image Super-Resolution (SR) methods assume that the
Low-Resolution (LR) images were artificially downsampled from High-Resolution
(HR) images with bicubic interpolation. This operation changes the natural
image characteristics and reduces noise. Hence, SR methods trained on such data
most often fail to produce good results when applied to real LR images. To
solve this problem, we propose a novel framework for generation of realistic
LR/HR training pairs. Our framework estimates realistic blur kernels, noise
distributions, and JPEG compression artifacts to generate LR images with
similar image characteristics as the ones in the source domain. This allows us
to train a SR model using high quality face images as Ground-Truth (GT). For
better perceptual quality we use a Generative Adversarial Network (GAN) based
SR model where we have exchanged the commonly used VGG-loss [24] with
LPIPS-loss [52]. Experimental results on both real and artificially corrupted
face images show that our method results in more detailed reconstructions with
less noise compared to existing State-of-the-Art (SoTA) methods. In addition,
we show that the traditional non-reference Image Quality Assessment (IQA)
methods fail to capture this improvement and demonstrate that the more recent
NIMA metric [16] correlates better with human perception via Mean Opinion Rank
(MOR).
