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
PAD-Net: A Perception-Aided Single Image Dehazing Network
In this work, we investigate the possibility of replacing the $\ell_2$ loss with perceptually derived loss functions (SSIM, MS-SSIM, etc.)
Deep-Energy: Unsupervised Training of Deep Neural Networks
The success of deep learning has been due, in no small part, to the availability of large annotated datasets.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e. g., object detection) of hazy images.
Progressive Feature Fusion Network for Realistic Image Dehazing
MKFMIKU/PFFNet
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Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium.
Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset
The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult.
Unsupervised Single Image Dehazing Using Dark Channel Prior Loss
Instead of feeding the network with synthetic data, we solely use real-world outdoor images and tune the network's parameters by directly minimizing the DCP.
Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization
In this paper, we present a novel image dehazing approach based on the optical model for haze images and regularized optimization.
Feature Forwarding for Efficient Single Image Dehazing
Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems.
DHSGAN: An End to End Dehazing Network for Fog and Smoke
rmalav15/DHSGAN
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ACCV2018 - Springer 2019
In this paper we propose a novel end-to-end convolution dehazing architecture, called De-Haze and Smoke GAN (DHSGAN).
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.
