Single Image Deraining
50 papers with code • 9 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Single Image Deraining
Libraries
Use these libraries to find Single Image Deraining models and implementations
Raindrop
Synthetic Rain Datasets
RainCityscapes
Real Rain Dataset
Latest papers with no code
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Single Image Deraining: From Model-Based to Data-Driven and Beyond
The goal of single-image deraining is to restore the rain-free background scenes of an image degraded by rain streaks and rain accumulation.
Confidence Measure Guided Single Image De-raining
Single image de-raining is an extremely challenging problem since the rainy images contain rain streaks which often vary in size, direction and density.
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.
Deep Single Image Deraining Via Estimating Transmission and Atmospheric Light in rainy Scenes
Instead of using the estimated atmospheric light directly to learn a network to calculate transmission, we utilize it as ground truth and design a simple but novel triangle-shaped network structure to learn atmospheric light for every rainy image, then fine-tune the network to obtain a better estimation of atmospheric light during the training of transmission network.
Depth-Attentional Features for Single-Image Rain Removal
Rain is a common weather phenomenon, where object visibility varies with depth from the camera and objects faraway are visually blocked more by fog than by rain streaks.
An Effective Two-Branch Model-Based Deep Network for Single Image Deraining
Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing.
A Deep Tree-Structured Fusion Model for Single Image Deraining
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem.
Unsupervised Single Image Deraining with Self-supervised Constraints
Most existing single image deraining methods require learning supervised models from a large set of paired synthetic training data, which limits their generality, scalability and practicality in real-world multimedia applications.
Semi-Supervised Translation with MMD Networks
This work aims to improve semi-supervised learning in a neural network architecture by introducing a hybrid supervised and unsupervised cost function.
