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.

本文提出了一种基于生成对抗网络的图像和人脸超分辨率的方法，通过训练高分辨率到低分辨率的GAN和低分辨率到高分辨率的GAN两个阶段，可以有效地提高实际应用场景下的低分辨率图像的质量，进而在人脸超分辨率领域取得了显著成果。

使用GAN学习图像超分辨率，先学习如何进行图像退化