Search Results for author: Wending Yan

Found 11 papers, 3 papers with code

Optical Flow in Dense Foggy Scenes using Semi-Supervised Learning

no code implementations CVPR 2020 Wending Yan, Aashish Sharma, Robby T. Tan

Initially, given a pair of synthetic fog images, its corresponding clean images and optical flow ground-truths, in one training batch we train our network in a supervised manner.

Optical Flow Estimation

Self-Aligned Video Deraining With Transmission-Depth Consistency

1 code implementation CVPR 2021 Wending Yan, Robby T. Tan, Wenhan Yang, Dengxin Dai

In this paper, we address the problems of rain streaks and rain accumulation removal in video, by developing a self-aligned network with transmission-depth consistency.

Optical Flow Estimation Rain Removal

Feature-Aligned Video Raindrop Removal with Temporal Constraints

no code implementations29 May 2022 Wending Yan, Lu Xu, Wenhan Yang, Robby T. Tan

Our single image module employs a raindrop removal network to generate initial raindrop removal results, and create a mask representing the differences between the input and initial output.

Optical Flow Estimation Rain Removal

Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal

1 code implementation6 Oct 2022 Yeying Jin, Wending Yan, Wenhan Yang, Robby T. Tan

Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.

Image Dehazing Image Enhancement +3

Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow

no code implementations CVPR 2023 Hanyu Zhou, Yi Chang, Wending Yan, Luxin Yan

To handle the practical optical flow under real foggy scenes, in this work, we propose a novel unsupervised cumulative domain adaptation optical flow (UCDA-Flow) framework: depth-association motion adaptation and correlation-alignment motion adaptation.

Domain Adaptation Optical Flow Estimation

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

1 code implementation3 Aug 2023 Yeying Jin, Beibei Lin, Wending Yan, Yuan Yuan, Wei Ye, Robby T. Tan

In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions.

NightRain: Nighttime Video Deraining via Adaptive-Rain-Removal and Adaptive-Correction

no code implementations1 Jan 2024 Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Shunli Zhang, Robby Tan

However, the intricacies of the real world, particularly with the presence of light effects and low-light regions affected by noise, create significant domain gaps, hampering synthetic-trained models in removing rain streaks properly and leading to over-saturation and color shifts.

Rain Removal

Semantic Segmentation in Multiple Adverse Weather Conditions with Domain Knowledge Retention

no code implementations15 Jan 2024 Xin Yang, Wending Yan, Yuan Yuan, Michael Bi Mi, Robby T. Tan

They struggle to acquire new knowledge while also retaining previously learned knowledge. To address these problems, we propose a semantic segmentation method for multiple adverse weather conditions that incorporates adaptive knowledge acquisition, pseudolabel blending, and weather composition replay.

Multi-target Domain Adaptation Semantic Segmentation +1

Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow

no code implementations31 Jan 2024 Hanyu Zhou, Yi Chang, Haoyue Liu, Wending Yan, Yuxing Duan, Zhiwei Shi, Luxin Yan

In appearance adaptation, we employ the intrinsic image decomposition to embed the auxiliary daytime image and the nighttime image into a reflectance-aligned common space.

Domain Adaptation Intrinsic Image Decomposition +1

NightHaze: Nighttime Image Dehazing via Self-Prior Learning

no code implementations12 Mar 2024 Beibei Lin, Yeying Jin, Wending Yan, Wei Ye, Yuan Yuan, Robby T. Tan

By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.

Image Dehazing Image Enhancement

Nighttime Defogging Using High-Low Frequency Decomposition and Grayscale-Color Networks

no code implementations ECCV 2020 Wending Yan, Robby T. Tan, Dengxin Dai

Given an RGB foggy nighttime image, our grayscale module takes the grayscale version of the image as input, and decomposes it into high and low frequency layers.

Vocal Bursts Intensity Prediction

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