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Yihao Liu
Researcher at Chinese Academy of Sciences
Publications - 49
Citations - 4024
Yihao Liu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 8, co-authored 25 publications receiving 2278 citations. Previous affiliations of Yihao Liu include Shenzhen University.
Papers
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Book ChapterDOI
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
TL;DR: ESRGAN as mentioned in this paper improves the perceptual loss by using the features before activation, which could provide stronger supervision for brightness consistency and texture recovery, and won the first place in the PIRM2018-SR Challenge (region 3).
Posted Content
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Xintao Wang,Ke Yu,Shixiang Wu,Jinjin Gu,Yihao Liu,Chao Dong,Chen Change Loy,Yu Qiao,Xiaoou Tang +8 more
TL;DR: This work thoroughly study three key components of SRGAN – network architecture, adversarial loss and perceptual loss, and improves each of them to derive an Enhanced SRGAN (ESRGAN), which achieves consistently better visual quality with more realistic and natural textures than SRGAN.
Proceedings ArticleDOI
RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-Resolution
TL;DR: Wen et al. as mentioned in this paper proposed a Super-Resolution Generative Adversarial Networks with Ranker (RankSRGAN) to optimize the generator in the direction of perceptual metrics.
Posted Content
RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution
TL;DR: This work 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, and shows that RankSRGAN achieves visually pleasing results and reaches state-of-the-art performance in perceptual metrics.
Book ChapterDOI
Conditional Sequential Modulation for Efficient Global Image Retouching
TL;DR: An extremely light-weight framework - Conditional Sequential Retouching Network (CSRNet) - for efficient global image retouching is proposed that achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively.