TY  - CONF
AU  - Patricia Suarez
AU  - Angel Sappa
AU  - Boris X. Vintimilla
AU  - Riad I. Hammoud
A2  - CVPRW
PY  - 2018//
TI  - Deep Learning based Single Image Dehazing
BT  - 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop
SP  - 1250
EP  - 12507
KW  - Gallium nitride
KW  - Atmospheric modeling
KW  - Generators
KW  - Generative adversarial networks
KW  - Convergence
KW  - Image color analysis
N2  - 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 clearimages 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.
L1  - http://refbase.cvc.uab.es/files/SSV2018d.pdf
UR  - http://dx.doi.org/10.1109/CVPRW.2018.00162
N1  - MSIAU; 600.086; 600.130; 600.122
ID  - Patricia Suarez2018
ER  -