Nonhomogeneous Image Dehazing
10 papers with code • 5 benchmarks • 2 datasets
NH-HAZE
SMOKE
Most implemented papers
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.
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.
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.
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.
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.
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.
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.
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.
DehazeDCT: Towards Effective Non-Homogeneous Dehazing via Deformable Convolutional Transformer
movingforward100/Dehazing_R
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CVPRW 2024
Image dehazing, a pivotal task in low-level vision, aims to restore the visibility and detail from hazy images.
