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Proceedings Paper

Learning adapted dictionaries for geometry and texture separation
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Paper Abstract

This article proposes a new method for image separation into a linear combination of morphological components. This method is applied to decompose an image into meaningful cartoon and textural layers and is used to solve more general inverse problems such as image inpainting. For each of these components, a dictionary is learned from a set of exemplar images. Each layer is characterized by a sparse expansion in the corresponding dictionary. The separation inverse problem is formalized within a variational framework as the optimization of an energy functional. The morphological component analysis algorithm allows to solve iteratively this optimization problem under sparsity-promoting penalties. Using adapted dictionaries learned from data allows to circumvent some difficulties faced by fixed dictionaries. Numerical results demonstrate that this adaptivity is indeed crucial to capture complex texture patterns.

Paper Details

Date Published: 20 September 2007
PDF: 12 pages
Proc. SPIE 6701, Wavelets XII, 67011T (20 September 2007); doi: 10.1117/12.731244
Show Author Affiliations
Gabriel Peyré, Univ. Paris Dauphine (France)
Jalal Fadili, GREYC, ENSICAEN (France)
Jean-Luc Starck, CEA Saclay (France)

Published in SPIE Proceedings Vol. 6701:
Wavelets XII
Dimitri Van De Ville; Vivek K. Goyal; Manos Papadakis, Editor(s)

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