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Authors: Patricia Vitoria ; Joan Sintes and Coloma Ballester

Affiliation: Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona and Spain

Keyword(s): Generative Models, Wasserstein GAN, Image Inpainting, Semantic Understanding.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Shape Representation and Matching ; Visual Attention and Image Saliency

Abstract: Image inpainting is the task of filling-in missing regions of a damaged or incomplete image. In this work we tackle this problem not only by using the available visual data but also by incorporating image semantics through the use of generative models. Our contribution is twofold: First, we learn a data latent space by training an improved version of the Wasserstein generative adversarial network, for which we incorporate a new generator and discriminator architecture. Second, the learned semantic information is combined with a new optimization loss for inpainting whose minimization infers the missing content conditioned by the available data. It takes into account powerful contextual and perceptual content inherent in the image itself. The benefits include the ability to recover large regions by accumulating semantic information even it is not fully present in the damaged image. Experiments show that the presented method obtains qualitative and quantitative top-tier results in diffe rent experimental situations and also achieves accurate photo-realism comparable to state-of-the-art works. (More)

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Paper citation in several formats:
Vitoria, P.; Sintes, J. and Ballester, C. (2019). Semantic Image Inpainting through Improved Wasserstein Generative Adversarial Networks. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 249-260. DOI: 10.5220/0007367902490260

@conference{visapp19,
author={Patricia Vitoria. and Joan Sintes. and Coloma Ballester.},
title={Semantic Image Inpainting through Improved Wasserstein Generative Adversarial Networks},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={249-260},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007367902490260},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Semantic Image Inpainting through Improved Wasserstein Generative Adversarial Networks
SN - 978-989-758-354-4
IS - 2184-4321
AU - Vitoria, P.
AU - Sintes, J.
AU - Ballester, C.
PY - 2019
SP - 249
EP - 260
DO - 10.5220/0007367902490260
PB - SciTePress

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