Y
Yu Qiao
Researcher at Chinese Academy of Sciences
Publications - 23
Citations - 1278
Yu Qiao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Image restoration. The author has an hindex of 11, co-authored 23 publications receiving 561 citations. Previous affiliations of Yu Qiao include Shenzhen University & Hong Kong Polytechnic University.
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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.
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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.
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Efficient Image Super-Resolution Using Pixel Attention
TL;DR: This work designs a lightweight convolutional neural network for image super resolution with a newly proposed pixel attention scheme that could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters.
Proceedings ArticleDOI
NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results
Jianrui Cai,Shuhang Gu,Radu Timofte,Lei Zhang,Xiao Liu,Ding Yukang,Dongliang He,Chao Li,Yi Fu,Shilei Wen,Ruicheng Feng,Jinjin Gu,Yu Qiao,Chao Dong,Dongwon Park,Se Young Chun,Sanghoon Yoon,Junhyung Kwak,Donghee Son,Syed Waqas Zamir,Aditya Arora,Salman H. Khan,Fahad Shahbaz Khan,Ling Shao,Zhengping Wei,Lei Liu,Hong Cai,Darui Li,Fujie Gao,Zheng Hui,Xiumei Wang,Xinbo Gao,Guoan Cheng,Ai Matsune,Qiuyu Li,Leilei Zhu,Huaijuan Zang,Shu Zhan,Yajun Qiu,Ruxin wang,Jiawei Li,Yongcheng Jing,Mingli Song,Pengju Liu,Kai Zhang,Jingdong Liu,Jiye Liu,Hongzhi Zhang,Wangmeng Zuo,Wenyi Tang,Jing Liu,Youngjung Kim,Changyeop Shin,Minbeom Kim,Sungho Kim,Pablo Navarrete Michelini,Hanwen Liu,Dan Zhu,Xuan Xu,Xin Li,Furui Bai,Xiaopeng Sun,Lin Zha,Yuanfei Huang,Wen Lu,Yanpeng Cao,Du Chen,Zewei He,Sun Anshun,Siliang Tang,Fan Hongfei,Xiang Li,Li Guo,Zhang Wenjie,Zhang Yumei,Qingwen He,Jinghui Qin,Lishan Huang,Yukai Shi,Pengxu Wei,Wushao Wen,Liang Lin,Jun Yu,Guochen Xie,Mengyan Li,Rong Chen,Xiaotong Luo,Chen Hong,Yanyun Qu,Cuihua Li,Zhi-Song Liu,Li-Wen Wang,Chu-Tak Li,Can Zhao,Bowen Li,Chung-Chi Tsai,Shang-Chih Chuang,Joon-Hee Choi,Joon-Soo Kim,Xiaoyun Jiang,Ze Pan,Qunbo Lv,Zheng Tan,Peidong He +103 more
TL;DR: The 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) is reviewed with a focus on proposed solutions and results and the state-of-the-art in real-world single image super- resolution.
Proceedings ArticleDOI
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic
TL;DR: Wang et al. as discussed by the authors proposed a new solution pipeline that combines classification and SR in a unified framework, which can help most existing methods (e.g., FSRCNN, CARN, SRResNet, RCAN) save up to 50% FLOPs on DIV8K datasets.