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Locally Parallel Textures Modeling with Adapted Hilbert Spaces

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Date
2009
Collection title
Lecture Notes in Computer Science
Collection Id
5681
Link to item file
http://hal.archives-ouvertes.fr/hal-00392270/en/
Dewey
Probabilités et mathématiques appliquées
Sujet
Texture; image separation; inpainting; total variation; variational method; local Fourier; wavelets; cartoon
DOI
http://dx.doi.org/10.1007/978-3-642-03641-5_32
Conference name
EMMCVPR'09
Conference date
08-2009
Conference city
Bonn
Conference country
Allemagne
Book title
Energy Minimization Methods in Computer Vision and Pattern Recognition. 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009. Proceedings
Author
Schmidt, Frank R.; Blake, Andrew; Boykov, Yuri; Cremers, Daniel
Publisher
Springer
Publisher city
Berlin
Year
2009
Pages number
494
ISBN
978-3-642-03640-8
Book URL
http://dx.doi.org/10.1007/978-3-642-03641-5
URI
https://basepub.dauphine.fr/handle/123456789/567
Collections
  • CEREMADE : Publications
Metadata
Show full item record
Author
Maurel, Pierre
Aujol, Jean-François
Peyré, Gabriel
Type
Communication / Conférence
Item number of pages
429-442
Abstract (EN)
This article presents a new adaptive texture model. Locally parallel oscillating patterns are modeled with a weighted Hilbert space defined over local Fourier coefficients. The weights on the local Fourier atoms are optimized to match the local orientation and frequency of the texture. We propose an adaptive method to decompose an image into a cartoon layer and a locally parallel texture layer using this model and a total variation cartoon model. This decomposition method is then used to denoise an image containing oscillating patterns. Finally we show how to take advantage of such a separation framework to simultaneously inpaint the structure and texture components of an image with missing parts. Numerical results show that our method improves state of the art algorithms for directional and complex textures.

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