Single Image Haze Removal
8 papers with code • 1 benchmarks • 2 datasets
RESIDE
D-HAZY
Most implemented papers
DehazeNet: An End-to-End System for Single Image Haze Removal
The key to achieve haze removal is to estimate a medium transmission map for an input hazy image.
Single Image Haze Removal Using Dark Channel Prior
The dark channel prior is a kind of statistics of outdoor haze-free images.
Single Image Haze Removal using a Generative Adversarial Network
Traditional methods to remove haze from images rely on estimating a transmission map.
Lower Bound on Transmission Using Non-Linear Bounding Function in Single Image Dehazing
The accuracy and effectiveness of SID depends on accurate value of transmission and atmospheric light.
Single Image Haze Removal Using Conditional Wasserstein Generative Adversarial Networks
We train a generative adversarial network to learn the probability distribution of clear images conditioned on the haze-affected images using the Wasserstein loss function, using a gradient penalty to enforce the Lipschitz constraint.
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.
PMHLD: Patch Map Based Hybrid Learning DehazeNet for Single Image Haze Removal
weitingchen83/Dehazing-PMHLD-Patch-Map-Based-Hybrid-Learning-DehazeNet-for-Single-Image-Haze-Removal-TIP-2020
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IEEE Transaction on Image Processing 2020
In addition, to further enhance the performance of the method for haze removal, a patch-map-based DCP has been embedded into the network, and this module has been trained with the atmospheric light generator, patch map selection module, and refined module simultaneously.
Task Adaptive Network for Image Restoration With Combined Degradation Factors
JingyuanZhou/Task_Adaptive_Network
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IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) 2022
Therefore, we propose a task-adaptive attention module to enable the network to restore images with multiple degradation factors.
