Image Dehazing
113 papers with code • 11 benchmarks • 16 datasets
( Image credit: Densely Connected Pyramid Dehazing Network )
Benchmarks
These leaderboards are used to track progress in Image Dehazing
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KITTI
O-HAZE
RESIDE
DENSE
I-HAZE
NH-HAZE
Haze4k
D-HAZY
Latest papers with no code
Visibility Enhancement for Low-light Hazy Scenarios
The simulation is designed for generating the dataset with ground-truths by the proposed low-light hazy imaging model.
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications.
DFR-Net: Density Feature Refinement Network for Image Dehazing Utilizing Haze Density Difference
In LB, we explore local density features from the dehazing residuals between hazy inputs and proposal images and introduce an Intermediate Dehazing Residual Feedforward (IDRF) module to update local features and pull them closer to clear image features.
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images
In this paper, we introduce Uni-Removal, a twostage semi-supervised framework for addressing the removal of multiple degradations in real-world images using a unified model and parameters.
RB-Dust -- A Reference-based Dataset for Vision-based Dust Removal
Because of this, we built a setup from which it is possible to take images from a stationary position close to the passing tractor.
SAGE-NDVI: A Stereotype-Breaking Evaluation Metric for Remote Sensing Image Dehazing Using Satellite-to-Ground NDVI Knowledge
Image dehazing is a meaningful low-level computer vision task and can be applied to a variety of contexts.
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.
Streamlined Global and Local Features Combinator (SGLC) for High Resolution Image Dehazing
For this kind of image, the model needs to work on a downscaled version of the image or on cropped patches from it.
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.
Reliable Image Dehazing by NeRF
In order to obtain real shot data in different scenes, we used fog generators, array cameras, mobile phones, underwater cameras and drones to obtain haze data.
