  Generative Adversarial Networks (GAN) have demonstrated the potential to
recover realistic details for single image super-resolution (SISR). To further
improve the visual quality of super-resolved results, PIRM2018-SR Challenge
employed perceptual metrics to assess the perceptual quality, such as PI, NIQE,
and Ma. However, existing methods cannot directly optimize these
indifferentiable perceptual metrics, which are shown to be highly correlated
with human ratings. To address the problem, we propose Super-Resolution
Generative Adversarial Networks with Ranker (RankSRGAN) to optimize generator
in the direction of different perceptual metrics. Specifically, we first train
a Ranker which can learn the behaviour of perceptual metrics and then introduce
a novel rank-content loss to optimize the perceptual quality. The most
appealing part is that the proposed method can combine the strengths of
different SR methods to generate better results. Furthermore, we extend our
method to multiple Rankers to provide multi-dimension constraints for the
generator. Extensive experiments show that RankSRGAN achieves visually pleasing
results and reaches state-of-the-art performance in perceptual metrics and
quality. Project page: https://wenlongzhang0517.github.io/Projects/RankSRGAN
