Nonhomogeneous Image Dehazing
9 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Nonhomogeneous Image Dehazing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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NH-HAZE
SMOKE
Latest papers
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
algolzw/image-restoration-sde
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This work aims to improve the applicability of diffusion models in realistic image restoration.
Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal
Few existing image defogging or dehazing methods consider dense and non-uniform particle distributions, which usually happen in smoke, dust and fog.
A Novel Encoder-Decoder Network with Guided Transmission Map for Single Image Dehazing
A novel Encoder-Decoder Network with Guided Transmission Map (EDN-GTM) for single image dehazing scheme is proposed in this paper.
Efficient Re-parameterization Residual Attention Network For Nonhomogeneous Image Dehazing
This paper proposes an end-to-end Efficient Re-parameterizationResidual Attention Network(ERRA-Net) to directly restore the nonhomogeneous hazy image.
Single image dehazing for a variety of haze scenarios using back projected pyramid network
ayu-22/BPPNet-Back-Projected-Pyramid-Network
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Learning to dehaze single hazy images, especially using a small training dataset is quite challenging.
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.
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
diptamath/Nonhomogeneous_Image_Dehazing
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IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020
Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
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.
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.
