Rain Removal
Leaderboards
Greatest papers with code
Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
CVPR 2018
• rui1996/DeRaindrop
• 
This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.
Image De-raining Using a Conditional Generative Adversarial Network
Hence, it is important to solve the problem of single image de-raining/de-snowing.
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
CVPR 2019
• stevewongv/SPANet
•
First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images.
Progressive Image Deraining Networks: A Better and Simpler Baseline
To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
Single Image Deraining: A Comprehensive Benchmark Analysis
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.
Gated Context Aggregation Network for Image Dehazing and Deraining
• cddlyf/GCANet
•
Image dehazing aims to recover the uncorrupted content from a hazy image.
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
CVPR 2019
• liruoteng/HeavyRainRemoval
•
This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks.
Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning
• liruoteng/HeavyRainRemoval
•
This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks.
A Survey on Rain Removal from Video and Single Image
The investigations on rain removal from video or a single image has thus been attracting much research attention in the field of computer vision and pattern recognition, and various methods have been proposed against this task in the recent years.
Semi-supervised Transfer Learning for Image Rain Removal
However, previous deep learning methods need to pre-collect a large set of image pairs with/without synthesized rain for training, which tends to make the neural network be biased toward learning the specific patterns of the synthesized rain, while be less able to generalize to real test samples whose rain types differ from those in the training data.