Single Image Dehazing
53 papers with code • 2 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Single Image Dehazing
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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O-HAZE
UIEB
RESIDE
I-HAZE
NH-HAZE
D-HAZY
SMOKE
Latest papers
Rethinking Performance Gains in Image Dehazing Networks
idkiro/gunet
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Image dehazing is an active topic in low-level vision, and many image dehazing networks have been proposed with the rapid development of deep learning.
LKD-Net: Large Kernel Convolution Network for Single Image Dehazing
The designed DLKCB can split the deep-wise large kernel convolution into a smaller depth-wise convolution and a depth-wise dilated convolution without introducing massive parameters and computational overhead.
Vision Transformers for Single Image Dehazing
Image dehazing is a representative low-level vision task that estimates latent haze-free images from hazy images.
Single UHD Image Dehazing via Interpretable Pyramid Network
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time.
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.
Image Dehazing Transformer With Transmission-Aware 3D Position Embedding
Li-Chongyi/Dehamer
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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.
TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions
jeya-maria-jose/TransWeather
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CVPR 2022
We also introduce a transformer decoder with learnable weather type embeddings to adjust to the weather degradation at hand.
Perceiving and Modeling Density is All You Need for Image Dehazing
Owen718/Perceiving-and-Modeling-Density-is-All-You-Need-for-Image-Dehazing
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However, due to the paradox caused by the variation of real captured haze and the fixed degradation parameters of the current networks, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose to solve this problem by perceiving and modeling density for uneven haze distribution.
Single Image Dehazing with An Independent Detail-Recovery Network
In this paper, we propose a single image dehazing method with an independent Detail Recovery Network (DRN), which considers capturing the details from the input image over a separate network and then integrates them into a coarse dehazed image.
Complementary Feature Enhanced Network with Vision Transformer for Image Dehazing
phoenixtreesky7/cfen-vit-dehazing
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In this paper, firstly, we propose a new complementary feature enhanced framework, in which the complementary features are learned by several complementary subtasks and then together serve to boost the performance of the primary task.
