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computer-vision-10

Image Dehazing

3260 papers • 126 benchmarks • 313 datasets

( Image credit: Densely Connected Pyramid Dehazing Network )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in image-dehazing-20

Trend

Dataset

Best Model

Actions

SOTS Indoor

SOTS Outdoor

Haze4k

Libraries

Use these libraries to find image-dehazing-20 models and implementations

Datasets

Subtasks

No subtasks available.

Most implemented papers

Contrastive Learning for Compact Single Image Dehazing

Lizhuang Ma, Haiyan Wu, Yanyun Qu, Shaohui Lin, J. Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie•Sun Apr 18 2021

This paper proposes 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 and develops a compact dehazing network based on autoencoder-like framework.

783

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

RS-Haze

RESIDE-6K

O-Haze

Dense-Haze

KITTI

NH-HAZE

I-Haze

RESIDE

D-HAZY

SQUID

RS-Haze

Paper Graph

GMAN: A Graph Multi-Attention Network for Traffic Prediction

Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, Jianzhong Qi•Sun Nov 10 2019

Experimental results on two real-world traffic prediction tasks demonstrate the superiority of GMAN, and in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure.

1722 0

Paper Graph

DehazeNet: An End-to-End System for Single Image Haze Removal

K. Jia, D. Tao, Xiangmin Xu, Bolun Cai, Chunmei Qing•Wed Jan 27 2016

This paper proposes a trainable end-to-end system called DehazeNet, for medium transmission estimation, which takes a hazy image as input, and outputs its medium transmission map that is subsequently used to recover a haze-free image via atmospheric scattering model.

2835 0

Paper Graph

Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing

Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, Jun Chen•Thu Oct 04 2018

This paper proposes an end-to-end generative method for image dehazing based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images.

20 0

Paper Graph

FFA-Net: Feature Fusion Attention Network for Single Image Dehazing

Zhiling Wang, Xu Qin, Yuanchao Bai, Xiaodong Xie, Huizhu Jia•Sun Nov 17 2019

The proposed FFA-Net surpasses previous state-of-the-art single image dehazing methods by a very large margin both quantitatively and qualitatively, boosting the best published PSNR metric from 30.23 dB to 36.39 dB on the SOTS indoor test dataset.

1689 0

Paper Graph

An All-in-One Network for Dehazing and Beyond

Zhangyang Wang, Boyi Li, Dan Feng, Xiulian Peng, Jizheng Xu•Wed Jul 19 2017

This paper proposes an image dehazing model built with a convolutional neural network (CNN), called All-in-One Dehazing Network (AOD-Net), designed based on a re-formulated atmospheric scattering model that directly generates the clean image through a light-weight CNN.

190 0

Paper Graph

I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images

Radu Timofte, C. Ancuti, Cosmin Ancuți, C. Vleeschouwer•Thu Apr 12 2018

A new dataset -named I-HAZE- that contains 35 image pairs of hazy and corresponding haze-free (ground-truth) indoor images that allows us to objectively compare the existing image dehazing techniques using traditional image quality metrics such as PSNR and SSIM.

423 0

Paper Graph

Single Image Haze Removal using a Generative Adversarial Network

N. BharathRaj, N. Venkateswaran•Sun Oct 21 2018

This paper proposes an end-to-end learning based approach which uses a modified conditional Generative Adversarial Network to directly remove haze from an image to demonstrate that the model performs competitively with the state of the art methods.

46 0

Paper Graph

Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing

H. K. Ekenel, Deniz Engin, Anil Genç•Sun May 13 2018

This paper presents 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, and improves CycleGAN method both quantitatively and qualitatively.

553 0

Paper Graph

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Image Dehazing | State-of-the-Art