PARISOTTO, Simone; CALATRONI, Luca; BUGEAU, Aurélie; PAPADAKIS, Nicolas; SCHÖNLIEB, Carola-Bibiane

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PARISOTTO, Simone
University of Cambridge [UK] [CAM]

CALATRONI, Luca
Morphologie et Images [MORPHEME]

BUGEAU, Aurélie
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

Langue

Article de revue

Ce document a été publié dans

IEEE Transactions on Image Processing. 2020, vol. 29, p. 5507-5516

Institute of Electrical and Electronics Engineers

Résumé en anglais

We propose a new variational model for nonlinear image fusion. Our approach incorporates the osmosis model proposed in Vogel et al. (2013) and Weickert et al. (2013) as an energy term in a variational model. The osmosis energy is known to realize visually plausible image data fusion. As a consequence, our method is invariant to multiplicative brightness changes. On the practical side, it requires minimal supervision and parameter tuning and can encode prior information on the structure of the images to be fused. We develop a primal-dual algorithm for solving this new image fusion model and we apply the resulting minimisation scheme to multi-modal image fusion for face fusion, colour transfer and some cultural heritage conservation challenges. Visual comparison to state-of-the-art proves the quality and flexibility of our method.< Réduire

Mots clés en anglais

Image fusion

Osmosis filtering

Cultural heritage imaging

Primal-dual algorithm

Non-convex optimisation

URI

https://oskar-bordeaux.fr/handle/20.500.12278/192747

Projet Européen

Nonlocal Methods for Arbitrary Data Sources

Project ANR

Generalized Optimal Transport Models for Image processing - ANR-16-CE33-0010
Repenser la post-production d'archives avec des méthodes à patch, variationnelles et par apprentissage - ANR-19-CE23-0027

Origine

Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251