31 citations · 71 across the 18 of their papers we have counts for
7 papers · 1 filter
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-…
Make Lossy Compression Meaningful for Low-Light Images
Shilv Cai, Liqun Chen, Sheng Zhong +3
Low-light images frequently occur due to unavoidable environmental influences or technical limitations, such as insufficient lighting or limited exposure time. To achieve better vi…
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow
Hanyu Zhou, Yi Chang, Gang Chen +1
Optical flow estimation has made great progress, but usually suffers from degradation under adverse weather. Although semi/full-supervised methods have made good attempts, the doma…
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
Hanyu Zhou, Yi Chang, Wending Yan +1
Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing method…