14 citations · 43 across the 8 of their papers we have counts for
8 papers
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…
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.…
Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning
Yeying Jin, Ruoteng Li, Wenhan Yang +1
Estimating the reflectance layer from a single image is a challenging task. It becomes more challenging when the input image contains shadows or specular highlights, which often re…
DeS3: Adaptive Attention-driven Self and Soft Shadow Removal using ViT Similarity
Yeying Jin, Wei Ye, Wenhan Yang +2
Removing soft and self shadows that lack clear boundaries from a single image is still challenging. Self shadows are shadows that are cast on the object itself. Most existing metho…
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…
Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression
Yeying Jin, Wenhan Yang, Robby T. Tan
Night images suffer not only from low light, but also from uneven distributions of light. Most existing night visibility enhancement methods focus mainly on enhancing low-light reg…
DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network
Yeying Jin, Aashish Sharma, Robby T. Tan
Shadow removal from a single image is generally still an open problem. Most existing learning-based methods use supervised learning and require a large number of paired images (sha…