Name
Abstract | ||
|---|---|---|
This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CVPRW.2018.00162 | 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | Field | DocType |
RGB images,color channel,multiple loss functions scheme,conditional probabilistic model,GAN architecture,conditioned entrance,fast model training convergence,homogeneous model generalization,high-quality clear images,stacked conditional generative adversarial network,deep learning based single image dehazing,haze degradation removal | Convergence (routing),Computer vision,Pattern recognition,Computer science,Homogeneous,Atmospheric model,RGB color model,Artificial intelligence,Statistical model,Deep learning,Channel (digital image),Haze | Conference |
ISSN | ISBN | Citations |
2160-7508 | 978-1-5386-6101-7 | 1 |
PageRank | References | Authors |
0.35 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patricia L. Suarez | 1 | 15 | 5.78 |
| Angel Domingo Sappa | 2 | 58 | 10.63 |
| Boris Xavier Vintimilla | 3 | 46 | 10.49 |
| Riad I. Hammoud | 4 | 118 | 9.46 |