Transformation of the Multivariate Generalized Gaussian Distribution for Image Editing

Hristina Hristova 1 Olivier Le Meur 2 Rémi Cozot 1 Kadi Bouatouch 1
1 FRVSense - FRVSense
IRISA-D6 - MEDIA ET INTERACTIONS
2 Sirocco - Analysis representation, compression and communication of visual data
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Multivariate generalized Gaussian distributions (MGGDs) have aroused a great interest in the image processing community thanks to their ability to describe accurately various image features, such as image gradient fields. However, so far their applicability has been limited by the lack of a transformation between two of these parametric distributions. In this paper, we propose a novel transformation between MGGDs, consisting of an optimal transportation of the second-order statistics and a stochastic-based shape parameter transformation. We employ the proposed transformation between MGGDs for a color transfer and a gradient transfer between images. We also propose a new simultaneous transfer of color and gradient, which we apply for image color correction.
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Article dans une revue
IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2017, pp.15. 〈10.1109/TVCG.2017.2769050〉
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https://hal.inria.fr/hal-01650368
Contributeur : Olivier Le Meur <>
Soumis le : mardi 28 novembre 2017 - 13:34:25
Dernière modification le : mercredi 16 mai 2018 - 11:24:14

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Hristina Hristova, Olivier Le Meur, Rémi Cozot, Kadi Bouatouch. Transformation of the Multivariate Generalized Gaussian Distribution for Image Editing. IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers, 2017, pp.15. 〈10.1109/TVCG.2017.2769050〉. 〈hal-01650368〉

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