Image Dehazing

88 papers with code • 11 benchmarks • 16 datasets

Most implemented papers

GMAN: A Graph Multi-Attention Network for Traffic Prediction

zhengchuanpan/GMAN 11 Nov 2019

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 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

caibolun/DehazeNet 28 Jan 2016

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 5 Oct 2018

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

zhilin007/FFA-Net 18 Nov 2019

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

soumik12345/AODNet 20 Jul 2017

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 13 Apr 2018

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

engindeniz/Cycle-Dehaze 14 May 2018

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

thatbrguy/Dehaze-GAN 22 Oct 2018

Traditional methods to remove haze from images rely on estimating a transmission map.