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Erschienen in:
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2014 | OriginalPaper | Buchkapitel

Mask-Specific Inpainting with Deep Neural Networks

verfasst von : Rolf Köhler, Christian Schuler, Bernhard Schölkopf, Stefan Harmeling

Erschienen in: Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Most inpainting approaches require a good image model to infer the unknown pixels. In this work, we directly learn a mapping from image patches, corrupted by missing pixels, onto complete image patches. This mapping is represented as a deep neural network that is automatically trained on a large image data set. In particular, we are interested in the question whether it is helpful to exploit the shape information of the missing regions, i.e. the masks, which is something commonly ignored by other approaches. In comprehensive experiments on various images, we demonstrate that our learning-based approach is able to use this extra information and can achieve state-of-the-art inpainting results. Furthermore, we show that training with such extra information is useful for blind inpainting, where the exact shape of the missing region might be uncertain, for instance due to aliasing effects.

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Vorheriges Kapitel Graph-Based and Variational Minimization of Statistical Cost Functionals for 3D Segmentation of Aortic Dissections
Nächstes Kapitel Detection of Clustered Objects in Sparse Point Clouds Through 2D Classification and Quadric Filtering
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Metadaten
Titel
Mask-Specific Inpainting with Deep Neural Networks
verfasst von
Rolf Köhler
Christian Schuler
Bernhard Schölkopf
Stefan Harmeling
Copyright-Jahr
2014
Buch
Pattern Recognition

Print ISBN: 978-3-319-11751-5

Electronic ISBN: 978-3-319-11752-2

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-11752-2_43

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