博士生导师，深圳理工大学人工智能研究院AI觉-知中心主任。中国科学院深圳先进技术研究院研究员，上海人工智能实验室领军科学家，上海交通大学兼职博导。博士毕业于香港中文大学信息工程专业，导师为汤晓鸥教授和吕建勤教授。2014年，在欧洲计算机视觉大会（ECCV）上发表论文SRCNN，首次将深度学习引入图像超分辨领域。2017年至今，多次带队参加国际超分辨率比赛，共获得9项冠军。2016年-2018年就职于商汤科技，带领商汤超分团队开发了世界首款基于深度学习的数码变焦软件。2021年起被斯坦福大学评选为世界前2%顶尖科学家。2022年起被评为AI 2000人工智能全球最具影响力学者。2023年获得上海市技术发明奖一等奖。2024年入选Elsevier发布的2023年中国高被引学者榜单。在顶级国际期刊和会议中发表相关论文100余篇，截止2025年2月，谷歌学术引用量超过4.3万次。

主要研究方向为底层计算机视觉，包括图像超分辨率、去噪和增强等。

学习经历

2012年8月-2016年12月，香港中文大学，博士

2007年9月-2011年7月，北京理工大学，本科

工作经历

2024年11月-至今，深圳理工大学，特聘教授，人工智能研究院 AI觉-知研究中心主任

2018年6月-至今，中国科学院深圳先进技术研究院，历任副研究员，研究员，多媒体集成技术研究中心主任

2022年1月-至今，上海人工智能实验室，领军科学家

2016年8月-2018年6月，深圳市商汤科技有限公司，高级研究经理

国际影响力

全球前2%顶尖科学家

AI 2000人工智能全球最具影响力学者

入选2023年“中国高被引学者”榜单（Elsevier发布）

所获荣誉

2023年上海市技术发明奖一等奖

在PIRM 2018、NTIRE 2019、AIM 2020、NTIRE 2021、NTIRE 2022等国挑战赛中获得9项冠军。

代表性论文

[1]Xiangyu Chen, Zheyuan Li, Yuandong Pu, Yihao Liu, Jiantao Zhou, Yu Qiao, Chao Dong. “A Comparative Study of Image Restoration Networks for General Backbone Network Design”. The European Conference on Computer Vision(ECCV), 2024.

[2]Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, Chao Dong. “Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild”. Computer Vision and Pattern Recognition(CVPR), 2024.

[3]Yuzhou Huang, Liangbin Xie, Xintao Wang, Ziyang Yuan, Xiaodong Cun, Yixiao Ge, Jiantao Zhou, Chao Dong, Rui Huang, Ruimao Zhang, Ying Shan. “SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models”. Computer Vision and Pattern Recognition(CVPR), 2024.

[4]Wenlong Zhang, Xiaohui Li, Xiangyu Chen, Yu Qiao, Xiao-Ming Wu, Chao Dong. “SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution”. International Conference on Learning Representations (ICLR spotlight), 2024.

[5]Yihao Liu, Hengyuan Zhao, Jinjin Gu, Yu Qiao, Chao Dong. “Evaluating the Generalization Ability of Super-Resolution Networks”. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 2023.(IF=23.6)

[6]Wenlong Zhang, Xiaohui Li, Guangyuan Shi, Xiangyu Chen, Yu Qiao, Xiao-Ming Wu, Chao Dong. “Real-World Image Super-Resolution as Multi-Task Learning”. Conference and Workshop on Neural Information Processing Systems(NeurIPS), 2023.

[7]Jinjin Gu, Xianzheng Ma, Xiangtao Kong, Yu Qiao, Chao Dong. “Networks are Slacking Off: Understanding Generalization Problem in Image Deraining”. Conference and Workshop on Neural Information Processing Systems(NeurIPS), 2023.

[8]Yihao Liu, Jingwen He, Jinjin Gu, Xiangtao Kong, Yu Qiao, Chao Dong. “DegAE: A New Pretraining Paradigm for Low-level Vision”. Computer Vision and Pattern Recognition (CVPR highlight), 2023.

[9]Xiangyu Chen, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong. “Activating More Pixels in Image Super-Resolution Transformer”. Computer Vision and Pattern Recognition (CVPR), 2023.

[10]Fanghua Yu, Xintao Wang, Mingdeng Cao, Gen Li, Ying Shan, Chao Dong. “OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer”. Computer Vision and Pattern Recognition (CVPR), 2023

[11]Shuwei Shi, Jinjin Gu, Liangbin Xie, Xintao Wang, Yujiu Yang, Chao Dong. “Rethinking Alignment in Video Super-Resolution Transformers”. Conference and Workshop on Neural Information Processing Systems(NeurIPS), 2022.

[12]Anran Liu, Yihao Liu, Jinjin Gu, Yu Qiao, Chao Dong. “Blind Image Super-Resolution: A Survey and Beyond”. IEEE Transactions on Pattern Analysis and Machine Intelligence(TPAMI), 2022.(IF=23.6)

[13]Yihao Liu, Jingwen He, Xiangyu Chen, Zhengwen Zhang, Hengyuan Zhao, Chao Dong, Yu Qiao. “Very Lightweight Photo Retouching Network with Conditional Sequential Modulation”. IEEE Transactions on Multimedia (TMM), 2022.

[14]Xiangtao Kong, Xina Liu, Jinjin Gu, Yu Qiao, Chao Dong. “Reflash Dropout in Image Super-Resolution”. Computer Vision and Pattern Recognition (CVPR), 2022.

[15]Jingwen He, Wu Shi, Kai Chen, Lean Fu, Chao Dong. “GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors”. Computer Vision and Pattern Recognition (CVPR), 2022.

[16]Liangbin Xie, Xintao Wang, Chao Dong, Zhongang Qi, Ying Shan. “Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution”. Conference and Workshop on Neural Information Processing Systems (NeurIPS Spotlight), 2021.

[17]Jingwen He, Chao Dong, Liu Yihao, Yu Qiao. “Interactive Multi-Dimension Modulation for Image Restoration”. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.(IF=23.6)

[18]Wenlong Zhang, Yihao Liu, Chao Dong, Yu Qiao. “RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank”. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.(IF=23.6)

[19]Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, Chen Change Loy. ”Esrgan: Enhanced super-resolution generative adversarial networks”. the European conference on computer vision (ECCV) workshops, 2018.

[20]Chao Dong, Chen Change Loy, Xiaoou Tang. “Accelerating the super-resolution convolutional neural network”. the European conference on computer vision (ECCV), 2016.

[21]Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. “Image super-resolution using deep convolutional networks”. IEEE transactions on pattern analysis and machine intelligence(TPAMI), 2015.

[22]Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang. “Learning a deep convolutional network for image super-resolution”. the European conference on computer vision (ECCV), 2014.

与上海交通大学、华为、咪咕视频合作，开发了基于先验自适应的视频超分算法，实现了对上世纪40-80年代超低分辨率视频的超分辨增强，被应用于中央广播电视总台、咪咕视讯、云视科技等龙业企头，支撑了229部影视经典和279段珍贵历史资料的超高清增强任务。其中，所复原的《开国大典》视频片段在央视建党百年庆典晚会上，用12x4k超大显示屏进行现场播放。中共一大纪念馆中的所有视频均通过该算法进行增强。联合申请的项目《真实世界视频智能增强技术及国产化应用》获得了2023年的上海市技术发明一等奖。