RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution

TL;DR

RankSRGAN introduces a novel GAN-based approach with a learned ranker and rank-content loss to directly optimize perceptual metrics, significantly enhancing the visual quality of super-resolved images.

Contribution

The paper proposes a new method that trains a ranker to optimize perceptual metrics directly, improving super-resolution results beyond existing techniques.

Findings

Achieves state-of-the-art perceptual quality in super-resolution

Generates visually pleasing, high-quality images

Effectively combines strengths of different SR methods

Abstract

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