Research
My current research interests include generative models and computational photography.
I enjoy the research that can benefit real-world applications.
Some of my papers have been used in some commercial products.
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KBNet: Kernel Basis Network for Image Restoration
Yi Zhang,
Dasong Li,
Xiaoyu Shi,
Dailan He,
Kangning Song,
Xiaogang Wang,
Honwei Qin,
Hongsheng Li
arXiv /
code
A general-purpose backbone for image restoration tasks (e.g. denoising, deraining, and
deblurring).
A novel kernel basis attention (KBA) module has been proposed to effectively aggregate the
spatial information via a
series of learnable kernel bases.
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No Attention is Needed: Grouped Spatial-temporal Shift for Simple and Efficient
Video Restorers
Dasong Li,
Xiaoyu Shi,
Yi Zhang,
Xiaogang Wang,
Honwei Qin,
Hongsheng Li
CVPR, 2023
arXiv /
code /
project
We propose a grouped spatial-temporal shift module for effective video restoration.
It surpasses previous SOTA methods with only 43% computational cost on video deblurring and
denoising.
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Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency
Denoising Network
Dasong Li*,
Yi Zhang*,
Ka Lung Law,
Xiaogang Wang,
Honwei Qin,
Hongsheng Li
IJCV, 2022
arXiv
A practical low-light imaging system for burst raw image denoising.
It has been deployed to some commercial smartphones.
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Learning Degradation Representations for Image Deblurring
Dasong Li,
Yi Zhang,
Ka Chun Cheung,
Xiaogang Wang,
Honwei Qin,
Hongsheng Li
ECCV, 2022
arXiv /
code
Learning degradation representations for blurry images. The learned degradation
representation
shows pleasing improvements on both image deblurring and reblurring.
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Self-Supervised Image Denoising via Iterative Data Refinement
Yi Zhang,
Dasong Li,
Ka Lung Law,
Xiaogang Wang,
Honwei Qin,
Hongsheng Li
CVPR, 2022
arXiv /
code /
SenseNoise dataset
A self-supervised image denoising method using noisy images and the noise model.
We achieve SOTA results on both real-world noise and synthetic noises (both point-wise and
spatial correlated noise types).
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Rethinking Noise Synthesis and Modeling in Raw Denoising
Yi Zhang,
Honwei Qin,
Xiaogang Wang,
Hongsheng Li
ICCV, 2021
arXiv /
code
A method to synthesize "almost the most realistic" raw image noise by sampling directly from
the real noise distribution.
We also found existing comparisons of noise synthesis methods are based on inaccurate noise
parameters.
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On Reinforcement Learning for Full-length Game of StarCraft
Zhen-Jia Pang,
Ruo-Ze Liu,
Zhou-Yu Meng,
Yi Zhang,
Yang Yu,
Tong Lu
AAAI, 2019   (Oral)
arXiv /
demo
We achieve over 93% winning rate of Protoss against the most difficult non-cheating
built-in AI (level-7) of Terran,
training within two days using a single machine with only 48 CPU cores and 8 K40 GPUs.
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Academic Services
Conference Reviewer: CVPR, ICCV, ECCV, ICML, NeurIPS, AAAI
Journal Reviewer: TIP
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Experience
Research intern at Snap Research, 2023.3 ~ Present
Research intern at LAMDA Group, 2017 ~ 2018
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Honors
Postgraduate Scholarship, the Chinese University of Hong Kong, 2019 ~ Present
Outstanding Graduate of Nanjing University, 2019
Merit student in Jiangsu Province, 2018
National Scholarship, 2018
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