2 citations · 4 across the 6 of their papers we have counts for
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Long-range Turbulence Mitigation: A Large-scale Dataset and A Coarse-to-fine Framework
Shengqi Xu, Run Sun, Yi Chang +3
Long-range imaging inevitably suffers from atmospheric turbulence with severe geometric distortions due to random refraction of light. The further the distance, the more severe the…
GT-Rain Single Image Deraining Challenge Report
Howard Zhang, Yunhao Ba, Ethan Yang +20
This report reviews the results of the GT-Rain challenge on single image deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to study the rainy weather phen…
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal
Yun Guo, Xueyao Xiao, Yi Chang +2
Learning-based image deraining methods have made great progress. However, the lack of large-scale high-quality paired training samples is the main bottleneck to hamper the real ima…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.2-Coded Target Restoration through Atmospheric Turbulence
Shengqi Xu, Shuning Cao, Haoyue Liu +3
In this technical report, we briefly introduce the solution of our team VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR 2023 UG+ Track 2.2. In t…
1st Solution Places for CVPR 2023 UG+ Challenge Track 2.1-Text Recognition through Atmospheric Turbulence
Shengqi Xu, Xueyao Xiao, Shuning Cao +2
In this technical report, we present the solution developed by our team VIELab-HUST for text recognition through atmospheric turbulence in Track 2.1 of the CVPR 2023 UG+ chal…
A Two-Stage Real Image Deraining Method for GT-RAIN Challenge CVPR 2023 Workshop UG+ Track 3
Yun Guo, Xueyao Xiao, Xiaoxiong Wang +3
In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG+ Track 3. In this task, we propose an efficient two-…