Single Image Dehazing

66 papers with code • 6 benchmarks • 8 datasets

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Most implemented papers

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.

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.

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.

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.

Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images

pmm09c/ntire-dehazing 5 Apr 2019

Characterized by dense and homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and corresponding haze-free images of various outdoor scenes.

Image Dehazing Transformer With Transmission-Aware 3D Position Embedding

Li-Chongyi/Dehamer CVPR 2022

Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.

Deep Variational Bayesian Modeling of Haze Degradation Process

imeunu/variational-dehazing-networks 4 Dec 2024

To account for such uncertainties and factors involved in haze degradation, we introduce a variational Bayesian framework for single image dehazing.

Single Image Dehazing via Multi-scale Convolutional Neural Networks

rwenqi/Multi-scale-CNN-Dehazing European Conference on Computer Vision 2016

The performance of existing image dehazing methods is limited by hand-designed features, such as the dark channel, color disparity and maximum contrast, with complex fusion schemes.