To learn image super-resolution, use a GAN to learn how to do image degradation first
This addresses the challenge of applying super-resolution to real-world images, which is incremental as it adapts existing GAN methods to a specific bottleneck.
The paper tackles the problem of image super-resolution failing on real-world low-quality images by proposing a two-stage GAN pipeline that first learns degradation from unpaired data and then super-resolves using paired data, reporting large improvements in face super-resolution over baselines.
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 efectively 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.