Image Dehazing
88 papers with code • 11 benchmarks • 16 datasets
( Image credit: Densely Connected Pyramid Dehazing Network )
Benchmarks
These leaderboards are used to track progress in Image Dehazing
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KITTI
O-HAZE
DENSE
RESIDE
I-HAZE
NH-HAZE
D-HAZY
SQUID
Most implemented papers
GMAN: A Graph Multi-Attention Network for Traffic Prediction
Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder.
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.
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
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.
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.
An All-in-One Network for Dehazing and Beyond
This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net).
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
inyong37/Vision
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This represents an important advantage of the I-HAZE dataset that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.
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.
Single Image Haze Removal using a Generative Adversarial Network
Traditional methods to remove haze from images rely on estimating a transmission map.
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.
