Image Dehazing
114 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
PANet: A Physics-guided Parametric Augmentation Net for Image Dehazing by Hazing
Image dehazing faces challenges when dealing with hazy images in real-world scenarios.
VIFNet: An End-to-end Visible-Infrared Fusion Network for Image Dehazing
Image dehazing poses significant challenges in environmental perception.
NightHaze: Nighttime Image Dehazing via Self-Prior Learning
By increasing the noise values to approach as high as the pixel intensity values of the glow and light effect blended images, our augmentation becomes severe, resulting in stronger priors.
Learning to Deblur Polarized Images
However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP.
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.
DHFormer: A Vision Transformer-Based Attention Module for Image Dehazing
The attention module then infers the spatial attention map before generating the final haze-free image.
Accurate and lightweight dehazing via multi-receptive-field non-local network and novel contrastive regularization
Thus, a multi-receptive-field non-local network (MRFNLN) consisting of the multi-stream feature attention block (MSFAB) and cross non-local block (CNLB) is presented in this paper.
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.
