Single Image Deraining
50 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
HINet: Half Instance Normalization Network for Image Restoration
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks.
Density-aware Single Image De-raining using a Multi-stream Dense Network
hezhangsprinter/DID-MDN
•
•
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.
Single Image Deraining: A Comprehensive Benchmark Analysis
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.
PMS-Net: Robust Haze Removal Based on Patch Map for Single Images
Conventional patch-based haze removal algorithms (e. g. the Dark Channel prior) usually performs dehazing with a fixed patch size.
Uncertainty Guided Multi-Scale Residual Learning-using a Cycle Spinning CNN for Single Image De-Raining
rajeevyasarla/UMRL--using-Cycle-Spinning
•
•
CVPR 2019
Previous approaches have attempted to address this problem by leveraging some prior information to remove rain streaks from a single image.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
A Model-driven Deep Neural Network for Single Image Rain Removal
hongwang01/RCDNet
•
•
CVPR 2020
Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model.
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal
weitingchen83/Dehazing-PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal-TIP-2020
•
•
IEEE Transaction on Image Processing 2020
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
Rethinking Image Deraining via Rain Streaks and Vapors
Single image deraining regards an input image as a fusion of a background image, a transmission map, rain streaks, and atmosphere light.
