Learning Adapted Dictionaries for Geometry and Texture Separation - Archive ouverte HAL Access content directly
Conference Papers Year : 2007

Learning Adapted Dictionaries for Geometry and Texture Separation

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.
Fichier principal
Vignette du fichier
MCALearnedSPIE07.pdf (397.68 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-00365601 , version 1 (03-03-2009)

Identifiers

  • HAL Id : hal-00365601 , version 1

Cite

Gabriel Peyré, Jalal M. Fadili, Jean-Luc Starck. Learning Adapted Dictionaries for Geometry and Texture Separation. SPIE Wavelets XII, Aug 2007, San Diego, CA, United States. pp.67011T. ⟨hal-00365601⟩
315 View
321 Download

Share

Gmail Facebook X LinkedIn More