RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
This addresses the challenge of improving visual quality in super-resolution for applications like image enhancement, though it is incremental as it builds on existing GAN methods.
The paper tackles the problem of optimizing indifferentiable perceptual metrics in single image super-resolution by proposing RankSRGAN, which uses a Ranker to learn these metrics and a rank-content loss, achieving state-of-the-art performance in perceptual metrics.
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 perceptual metrics. Specifically, we first train a Ranker which can learn the behavior 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. Extensive experiments show that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics. Project page: https://wenlongzhang0724.github.io/Projects/RankSRGAN