Image Dehazing

58 papers with code • 8 benchmarks • 13 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.

Uformer: A General U-Shaped Transformer for Image Restoration

ZhendongWang6/Uformer 6 Jun 2021

Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.

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.

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.

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.

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.