Rain Removal
61 papers with code • 1 benchmarks • 3 datasets
Libraries
Use these libraries to find Rain Removal models and implementations
Raindrop
Real Rain Dataset
LasVR
Most implemented papers
Image De-raining Using a Conditional Generative Adversarial Network
Hence, it is important to solve the problem of single image de-raining/de-snowing.
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.
Rain Removal in Traffic Surveillance: Does it Matter?
We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow.
Uformer: A General U-Shaped Transformer for Image Restoration
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Attentive Generative Adversarial Network for Raindrop Removal from a Single Image
rui1996/DeRaindrop
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CVPR 2018
This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.
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.
Deep Joint Rain Detection and Removal from a Single Image
jiupinjia/deep-adversarial-decomposition
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CVPR 2017
Based on the first model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output.
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.
