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Locally Parallel Textures Modeling with Adapted Hilbert Spaces

Maurel, Pierre; Aujol, Jean-François; Peyré, Gabriel (2009), Locally Parallel Textures Modeling with Adapted Hilbert Spaces, in Schmidt, Frank R.; Blake, Andrew; Boykov, Yuri; Cremers, Daniel, Energy Minimization Methods in Computer Vision and Pattern Recognition. 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009. Proceedings, Springer : Berlin, p. 429-442. http://dx.doi.org/10.1007/978-3-642-03641-5_32

Type
Communication / Conférence
External document link
http://hal.archives-ouvertes.fr/hal-00392270/en/
Date
2009
Conference title
EMMCVPR'09
Conference date
2009-08
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
Book author
Schmidt, Frank R.; Blake, Andrew; Boykov, Yuri; Cremers, Daniel
Publisher
Springer
Series title
Lecture Notes in Computer Science
Series number
5681
Published in
Berlin
ISBN
978-3-642-03640-8
Number of pages
494
Pages
429-442
Publication identifier
http://dx.doi.org/10.1007/978-3-642-03641-5_32
Metadata
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Author(s)
Maurel, Pierre cc
Aujol, Jean-François
Peyré, Gabriel
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.
Subjects / Keywords
Texture; image separation; inpainting; total variation; variational method; local Fourier; wavelets; cartoon

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