Single Image Deraining
32 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
Most implemented papers
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Restormer: Efficient Transformer for High-Resolution Image Restoration
swz30/restormer
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Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Pre-Trained Image Processing Transformer
huawei-noah/Pretrained-IPT
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CVPR 2021
To maximally excavate the capability of transformer, we present to utilize the well-known ImageNet benchmark for generating a large amount of corrupted image pairs.
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.
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
stevewongv/SPANet
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CVPR 2019
Second, to better cover the stochastic distribution of real rain streaks, we propose a novel SPatial Attentive Network (SPANet) to remove rain streaks in a local-to-global manner.
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.
Clearing the Skies: A deep network architecture for single-image rain removal
jinnovation/rainy-image-dataset
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We introduce a deep network architecture called DerainNet for removing rain streaks from an image.
007: Democratically Finding The Cause of Packet Drops
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.
Density-aware Single Image De-raining using a Multi-stream Dense Network
hezhangsprinter/DID-MDN
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CVPR 2018
In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method.
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.
