Rain Removal

87 papers with code • 1 benchmarks • 3 datasets

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Use these libraries to find Rain Removal models and implementations
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Most implemented papers

Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer CVPR 2022

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.

Image De-raining Using a Conditional Generative Adversarial Network

nekitmm/starnet 21 Jan 2017

Hence, it is important to solve the problem of single image de-raining/de-snowing.

Multi-Stage Progressive Image Restoration

swz30/MPRNet CVPR 2021

At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.

Pre-Trained Image Processing Transformer

huawei-noah/Pretrained-IPT 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?

aauvap/rainremoval 30 Oct 2018

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

csdwren/PReNet CVPR 2019

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 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 CVPR 2018

This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.

Deep Joint Rain Detection and Removal from a Single Image

jiupinjia/deep-adversarial-decomposition 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.

Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset

stevewongv/SPANet 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.