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
TypeCommunication / Conférence
External document linkhttp://hal.archives-ouvertes.fr/hal-00392270/en/
Book titleEnergy Minimization Methods in Computer Vision and Pattern Recognition. 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009. Proceedings
Book authorSchmidt, Frank R.; Blake, Andrew; Boykov, Yuri; Cremers, Daniel
Series titleLecture Notes in Computer Science
Number of pages494
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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 / KeywordsTexture; image separation; inpainting; total variation; variational method; local Fourier; wavelets; cartoon
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