Single Image Dehazing
52 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 with no code
Depth-agnostic Single Image Dehazing
To overcome the problem, we propose a simple yet novel synthetic method to decouple the relationship between haze density and scene depth, by which a depth-agnostic dataset (DA-HAZE) is generated.
SimHaze: game engine simulated data for real-world dehazing
Using a modern game engine, our approach renders crisp clean images and their precise depth maps, based on which high-quality hazy images can be synthesized for training dehazing models.
Dehazing-NeRF: Neural Radiance Fields from Hazy Images
Our method simulates the physical imaging process of hazy images using an atmospheric scattering model, and jointly learns the atmospheric scattering model and a clean NeRF model for both image dehazing and novel view synthesis.
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL).
Encoder-Decoder Network with Guided Transmission Map: Architecture
An insight into the architecture of the Encoder-Decoder Network with Guided Transmission Map (EDN-GTM), a novel and effective single image dehazing scheme, is presented in this paper.
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning
In contrast, we present a domain generalization framework based on meta-learning to dig out representative and discriminative internal properties of real hazy domains without test-time training.
Dual-former: Hybrid Self-attention Transformer for Efficient Image Restoration
Recently, image restoration transformers have achieved comparable performance with previous state-of-the-art CNNs.
Dual-Scale Single Image Dehazing Via Neural Augmentation
Model-based single image dehazing algorithms restore haze-free images with sharp edges and rich details for real-world hazy images at the expense of low PSNR and SSIM values for synthetic hazy images.
Towards Efficient Single Image Dehazing and Desnowing
A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compact architecture and three novel components.
Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction
To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios.
