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Author Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla; Riad I. Hammoud
Title Deep Learning based Single Image Dehazing Type Conference Article
Year 2018 Publication 14th IEEE Workshop on Perception Beyond the Visible Spectrum – In conjunction with CVPR 2018. Salt Lake City, Utah. USA Abbreviated Journal
Volume Issue Pages
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.
Corporate Author Thesis
Publisher Place of Publication (up) Editor
Language Summary Language Original Title
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Notes Approved no
Call Number gtsi @ user @ Serial 83
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