Approche variationnelle pour la déconvolution rapide de données 3D en microscopie biphotonique

Abstract : Better understanding of biological processes requires new, improved, high resolution imagery techniques. The present work concerns the restoration of data acquired with two-photon microscopy in biological tissue, in-vivo in particular. Two main challenges to tackle are: the large dimensionality of the acquired data and the incomplete knowledge of the impulse response of the system. We propose here an experimental setting to estimate it based on the observation of fluorescent micro-beads. The non-blind formulation of the related inverse problem of image restoration is then solved by minimizing a penalized criterion using an efficient convex optimization strategy based on the Majoration-Minimization approach. The effectiveness of the proposed approach is not only shown on simulated data but also on real data.
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Emilie Chouzenoux, Lisa Lamasse, Sandrine Anthoine, Caroline Chaux, Alexandre Jaouen, et al.. Approche variationnelle pour la déconvolution rapide de données 3D en microscopie biphotonique. Actes du 25e colloque GRETSI, Sep 2015, Lyon, France. ⟨hal-01278102⟩

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