Single Image Dehazing
66 papers with code • 6 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Single Image Dehazing
UIEB
RESIDE
I-HAZE
NH-HAZE
D-HAZY
SMOKE
RB-Dust
Most implemented papers
Contrastive Learning for Compact Single Image Dehazing
GlassyWu/AECR-Net
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CVPR 2021
In this paper, we propose a novel contrastive regularization (CR) built upon contrastive learning to exploit both the information of hazy images and clear images as negative and positive samples, respectively.
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
The FFA-Net architecture consists of three key components: 1) A novel Feature Attention (FA) module combines Channel Attention with Pixel Attention mechanism, considering that different channel-wise features contain totally different weighted information and haze distribution is uneven on the different image pixels.
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing
In this paper, we present an end-to-end network, called Cycle-Dehaze, for single image dehazing problem, which does not require pairs of hazy and corresponding ground truth images for training.
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Seanforfun/GMAN_Net_Haze_Removal
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Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis.
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.
Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing
The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Li-Chongyi/Dehamer
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CVPR 2022
Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.
Deep Variational Bayesian Modeling of Haze Degradation Process
imeunu/variational-dehazing-networks
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To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing.
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Single image haze removal has been a challenging problem due to its ill-posed nature.
Single Image Dehazing via Multi-scale Convolutional Neural Networks
The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes.
