To learn image super-resolution, use a GAN tolearn how to do image degradation first
Abstract. This paper is on image and face super-resolution. The vast
majority of prior work for this problem focus on how to increase the
resolution of low-resolution images which are artificially generated by
simple bilinear down-sampling (or in a few cases by blurring followed by
down-sampling). We show that such methods fail to produce good results
when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a two-stage process which firstly trains a
High-to-Low Generative Adversarial Network (GAN) to learn how to degrade and downsample high-resolution images requiring, during training,
only unpaired high and low-resolution images. Once this is achieved, the
output of this network is used to train a Low-to-High GAN for image
super-resolution using this time paired low- and high-resolution images.
Our main result is that this network can be now used to effectively increase the quality of real-world low-resolution images. We have applied
the proposed pipeline for the problem of face super-resolution where we
report large improvement over baselines and prior work although the
proposed method is potentially applicable to other object categories.
Keywords: Image and face super-resolution, Generative Adversarial
Networks, GANs.