Search Results for author: Wending Yan

Found 6 papers, 2 papers with code

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

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

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

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

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

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

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