Chongyi Li
Also known as Chong-Yi Li, Li Chong-yi
Chongyi Li is a computer-vision researcher and professor in Nankai University's College of Computer Science and at Shenzhen Loop Area Institute whose deep-learning work restores underwater and low-light images. After earning a PhD at Tianjin University in 2018 under Jichang Guo, he worked as a research fellow at City University of Hong Kong and Nanyang Technological University from 2018 to 2021 and as a research assistant professor at NTU from 2021 to 2023. In 2019, he coauthored the UWCNN underwater enhancement network and the UIEB benchmark paper with its Water-Net baseline, followed by Zero-Reference Deep Curve Estimation (Zero-DCE) at CVPR 2020 and a 2021 low-light enhancement survey and mobile-phone dataset. He shared the 2025 IEEE Signal Processing Society Best Paper Award for the UIEB benchmark paper and leads the π Research Group at Nankai University, working on computational imaging, generative vision and multimodal models.
- Now at
- Nankai University, Shenzhen Loop Area Institute and Nanyang Technological University
- Works
- 191
- Citations
- 21,347
- h-index
- 59
Publication numbers from OpenAlex, as of 22 October 2026.
Timeline
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2018
A PhD at Tianjin University
Chongyi Li earns a PhD at Tianjin University in 2018 under the supervision of Jichang Guo.
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2018–2021
Research fellow at CityU and NTU
From 2018 to 2021, he works as a research fellow at City University of Hong Kong under Sam Kwong and at Nanyang Technological University under Chen Change Loy.
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2019
UWCNN and the UIEB benchmark
In 2019, he coauthors UWCNN, an underwater image enhancement convolutional neural network built on an underwater scene prior. His 2019 UIEB paper introduces an underwater image enhancement benchmark dataset and proposes Water-Net as a trained CNN baseline.
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2020
Zero-DCE for low-light images
At CVPR 2020, he coauthors Zero-Reference Deep Curve Estimation (Zero-DCE), a low-light enhancer that formulates light enhancement as image-specific curve estimation with a deep network and requires no paired or unpaired training data.
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2021
Low-light enhancement survey and dataset
He coauthors a 2021 survey of deep-learning methods for low-light image and video enhancement that proposes a dataset of images and videos captured by different mobile-phone cameras under diverse illumination conditions.
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2021–2023
Research assistant professor at NTU
From 2021 to 2023, Li serves as a research assistant professor in the School of Computer Science and Engineering at Nanyang Technological University in Singapore.
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2025
IEEE SPS Best Paper Award
With Chunle Guo, Runmin Cong, Junhui Hou and Sam Kwong, he shares the 2025 IEEE Signal Processing Society Best Paper Award for their 2019 IEEE Transactions on Image Processing paper on the UIEB benchmark.
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As of October 2026
Professor at Nankai and SLAI
As of October 2026, he is a professor in Nankai University's College of Computer Science, where he leads the π Research Group, and serves concurrently as a professor at Shenzhen Loop Area Institute. His research covers computational imaging, image enhancement and restoration, generative vision, multimodal models and visual intelligence.
Career
- Since 2023Nankai UniversityProfessor
- NowShenzhen Loop Area InstituteProfessor
- 2020–2022Tongji University
- 2018–2021City University of Hong KongResearch fellow
- 2018–2023Nanyang Technological UniversityResearch fellow, Research assistant professor
Education
- 2018Tianjin UniversityPhD
Sources and credits
Photo: We have no published photo of Chongyi Li yet. Send us a free-licensed photo.
- People | Photography & Imaging (π) Research Group
- Underwater Scene Prior Inspired Deep Underwater Image and Video Enhancement
- An Underwater Image Enhancement Benchmark Dataset and Beyond
- Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
- Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
- Low-Light Image and Video Enhancement Using Deep Learning: A Survey
- Staff Achievements | Department of Computer Science
- Chongyi Li — Academic Homepage

