Single Image Haze Removal
8 papers with code • 1 benchmarks • 2 datasets
RESIDE
D-HAZY
Latest papers with no code
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Single-image haze removal is a long-standing hurdle for computer vision applications.
Single image dehazing via combining the prior knowledge and CNNs
If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally.
Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation
Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments.
Night Time Haze and Glow Removal using Deep Dilated Convolutional Network
The night haze removal is a severely ill-posed problem especially due to the presence of various visible light sources with varying colors and non-uniform illumination.
An Image dehazing approach based on the airlight field estimation
This paper proposes a scheme for single image haze removal based on the airlight field (ALF) estimation.
Semantic Single-Image Dehazing
In experiments, we validate our ap- proach upon synthetic and real hazy images, where our method showed superior performance over state-of-the-art approaches, suggesting semantic information facilitates the haze removal task.
C2MSNet: A Novel approach for single image haze removal
In this paper, a cardinal (red, green and blue) color fusion network for single image haze removal is proposed.
CANDY: Conditional Adversarial Networks based Fully End-to-End System for Single Image Haze Removal
In this paper, we present CANDY (Conditional Adversarial Networks based Dehazing of hazY images), a fully end-to-end model which directly generates a clean haze-free image from a hazy input image.
Joint Transmission Map Estimation and Dehazing using Deep Networks
Single image haze removal is an extremely challenging problem due to its inherent ill-posed nature.
