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Found 6 papers, 2 papers with code
Nighttime Defogging Using High-Low Frequency Decomposition and Grayscale-Color Networks
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.
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow
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.
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
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
Feature-Aligned Video Raindrop Removal with Temporal Constraints
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.
Self-Aligned Video Deraining With Transmission-Depth Consistency
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 in Dense Foggy Scenes using Semi-Supervised Learning
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.
