Extending alpha-expansion to a larger set of regularization functionsReport as inadecuate




Extending alpha-expansion to a larger set of regularization functions - Download this document for free, or read online. Document in PDF available to download.

1 IFSTTAR-COSYS-LEPSIS - Laboratoire Exploitation, Perception, Simulateurs et Simulations 2 MATIS - Méthodes d-Analyses pour le Traitement d-Images et la Stéréorestitution

Abstract : Many problems of image processing lead to the minimization of an energy, which is a function of one or several given images, with respect to a binary or multi-label image. When this energy is made of unary data terms and of pairwise regularization terms, and when the pairwise regularization term is a metric, the multi-label energy can be minimized quite rapidly, using the so-called ±-expansion algorithm. ±-expansion consists in decomposing the multi-label optimization into a series of binary sub-problems called move. Depending on the chosen decomposition, a different condition on the regularization term applies. The metric condition for ±-expansion move is rather restrictive. In many cases, the statistical model of the problem leads to an energy which is not a metric. Based on the enlightening article 1, we derive another condition for ±-jump move. Finally, we propose an alternated scheme which can be used even if the energy fulfills neither the ±-expansion nor ²-jump condition. The proposed scheme applies to a much larger class of regularization functions, compared to ±-expansion. This opens many possibilities of improvements on diverse image processing problems. We illustrate the advantages of the proposed optimization scheme on the image noise reduction problem.

en fr

Keywords : COUPE DE GRAPHE

Mots-clés : DEBRUITAGE OPTIMISATION IMAGE





Author: Mathias Paget - Jean-Philippe Tarel - Laurent Caraffa -

Source: https://hal.archives-ouvertes.fr/



DOWNLOAD PDF




Related documents