Rain Removal
165 papers with code • 2 benchmarks • 7 datasets
Libraries
Use these libraries to find Rain Removal models and implementations
Raindrop
RainDS
Real Rain Dataset
VRDS
Nightrain
HRI
Most implemented papers
Restormer: Efficient Transformer for High-Resolution Image Restoration
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.
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.
Image De-raining Using a Conditional Generative Adversarial Network
nekitmm/starnet
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Hence, it is important to solve the problem of single image de-raining/de-snowing.
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.
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.
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.
Uformer: A General U-Shaped Transformer for Image Restoration
ZhendongWang6/Uformer
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CVPR 2022
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.
NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining
In this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net (NASNet), which can solve all types of rain model tasks efficiently.
Multi-Scale Progressive Fusion Network for Single Image Deraining
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.
