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.
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.
1 code implementation • 6 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.
Ranked #1 on
Image Dehazing
on O-Haze
no code implementations • 29 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.
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.
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.