Rain Removal

61 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

Image De-raining Using a Conditional Generative Adversarial Network

hezhangsprinter/ID-CGAN 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.

Restormer: Efficient Transformer for High-Resolution Image Restoration

swz30/restormer 18 Nov 2021

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?

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.

Uformer: A General U-Shaped Transformer for Image Restoration

ZhendongWang6/Uformer 6 Jun 2021

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.

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.

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.

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.

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.