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Sparse Modeling of Textures

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Date
2009
Link to item file
http://hal.archives-ouvertes.fr/hal-00359747/en/
Dewey
Probabilités et mathématiques appliquées
Sujet
Inpainting; Learning dictionaries; Sparse representation; Texture synthesis; Image processing
Journal issue
Journal of Mathematical Imaging and Vision
Volume
34
Number
1
Publication date
05-2009
Article pages
17-31
Publisher
Springer
DOI
http://dx.doi.org/10.1007/s10851-008-0120-3
URI
https://basepub.dauphine.fr/handle/123456789/542
Collections
  • CEREMADE : Publications
Metadata
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Author
Peyré, Gabriel
Type
Article accepté pour publication ou publié
Abstract (EN)
This paper presents a generative model for textures that uses a local sparse description of the image content. This model enforces the sparsity of the expansion of local texture patches on adapted atomic elements. The analysis of a given texture within this framework performs the sparse coding of all the patches of the texture into the dictionary of atoms. Conversely, the synthesis of a new texture is performed by solving an optimization problem that seeks for a texture whose patches are sparse in the dictionary. This paper explores several strategies to choose this dictionary. A set of hand crafted dictionaries composed of edges, oscillations, lines or crossings elements allows to synthesize synthetic images with geometric features. Another option is to define the dictionary as the set of all the patches of an input exemplar. This leads to computer graphics methods for synthesis and shares some similarities with non-local means filtering. The last method we explore learns the dictionary by an optimization process that maximizes the sparsity of a set of exemplar patches. Applications of all these methods to texture synthesis, inpainting and classification shows the efficiency of the proposed texture model.

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