Single Image Dehazing
52 papers with code • 2 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Single Image Dehazing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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O-HAZE
UIEB
RESIDE
I-HAZE
NH-HAZE
D-HAZY
SMOKE
Most implemented papers
FAMED-Net: A Fast and Accurate Multi-scale End-to-end Dehazing Network
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks.
GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing
proteus1991/GridDehazeNet
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ICCV 2019
The proposed hazing method does not rely on the atmosphere scattering model, and we provide an explanation as to why it is not necessarily beneficial to take advantage of the dimension reduction offered by the atmosphere scattering model for image dehazing, even if only the dehazing results on synthetic images are concerned.
Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
dectrfov/Y-net
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Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms.
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Apart from these, several image manipulation techniques using these plugins have been compiled and demonstrated in the YouTube channel (https://youtube. com/user/kritiksoman) with the objective of demonstrating the use-cases for machine learning based image modification.
You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network
XLearning-SCU/2021-IJCV-YOLY
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In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained).
Progressive Update Guided Interdependent Networks for Single Image Dehazing
The estimated parameters are then used to guide our dehazing module, where the estimates are progressively updated by novel convolutional networks.
Single image dehazing for a variety of haze scenarios using back projected pyramid network
ayu-22/BPPNet-Back-Projected-Pyramid-Network
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Learning to dehaze single hazy images, especially using a small training dataset is quite challenging.
An Improved Air-Light Estimation Scheme for Single Haze Images Using Color Constancy Prior
Hazy environment attenuates the scene radiance and causes difficulty in distinguishing the color and texture of the scene.
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media
princeton-computational-imaging/ZeroScatter
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CVPR 2021
Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets.
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
whrws/nhrnet
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In this paper, we propose a Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph reasoning.
