11 citations · 31 across the 10 of their papers we have counts for
10 papers
NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results
Xin Li, Yeying Jin, Suhang Yao +95
This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this c…
RIRF: Reasoning Image Restoration Framework
Wending Yan, Rongkai Zhang, Kaihua Tang +2
Universal image restoration (UIR) aims to recover clean images from diverse and unknown degradations using a unified model. Existing UIR methods primarily focus on pixel reconstruc…
NightHaze: Nighttime Image Dehazing via Self-Prior Learning
Beibei Lin, Yeying Jin, Wending Yan +3
Masked autoencoder (MAE) shows that severe augmentation during training produces robust representations for high-level tasks. This paper brings the MAE-like framework to nighttime…
Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention
Xin Yang, Wending Yan, Yuan Yuan +2
Semantic segmentation's performance is often compromised when applied to unlabeled adverse weather conditions. Unsupervised domain adaptation is a potential approach to enhancing t…
NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction
Beibei Lin, Yeying Jin, Wending Yan +4
Existing deep-learning-based methods for nighttime video deraining rely on synthetic data due to the absence of real-world paired data. However, the intricacies of the real world,…
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
Yeying Jin, Beibei Lin, Wending Yan +3
Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources.…
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…
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Yeying Jin, Wending Yan, Wenhan Yang +1
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog. Dealing with these dense and/or…
Feature-Aligned Video Raindrop Removal with Temporal Constraints
Wending Yan, Lu Xu, Wenhan Yang +1
Existing adherent raindrop removal methods focus on the detection of the raindrop locations, and then use inpainting techniques or generative networks to recover the background beh…
Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning
Wending Yan, Aashish Sharma, Robby T. Tan
In dense foggy scenes, existing optical flow methods are erroneous. This is due to the degradation caused by dense fog particles that break the optical flow basic assumptions such…