中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
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RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
Wenlong Zhang; Yihao Liu; Chao Dong; Yu Qiao
2019
Conference NameICCV2019 oral
Conference Date2019
Conference Place韩国首尔
AbstractGenerative 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
Department多媒体
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.siat.ac.cn/handle/172644/15912
Collection数字所
Recommended Citation
GB/T 7714
Wenlong Zhang,Yihao Liu,Chao Dong,et al. RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution[C],2019.
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