Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences

Jérôme Boulanger; Charles Kervrann; Patrick Bouthemy; Peter Elbau; Jean-Baptiste Sibarita; Jean Salamero

Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences

Jérôme Boulanger ^{1, 2} Charles Kervrann ^{3, 4} Patrick Bouthemy ⁵ Peter Elbau ² Jean-Baptiste Sibarita ^{1, 6} Jean Salamero ^{1, 7}

1 PICT-IBiSA - BioImaging Cell and Tissue Core Facility

2 RICAM - Johann Radon Institute for Computational and Applied Mathematics

3 Unité MIAJ - INRA - Mathématiques et Informatique Appliquées

4 SERPICO - Space-timE RePresentation, Imaging and cellular dynamics of molecular COmplexes

Inria Rennes – Bretagne Atlantique

5 VISTAS - Spatio-Temporal Vision and Learning

IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique

6 PCS - Physiologie cellulaire de la synapse

7 Mécanismes moléculaires du transport intracellulaire

CDC - Compartimentation et dynamique cellulaires

Abstract : We present a nonparametric regression method for denoising 3-D image sequences acquired via fluorescence microscopy. The proposed method exploits the redundancy of the 3-D+time information to improve the signal-to-noise ratio of images corrupted by Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to remove the dependence between the mean and variance of intensity values. This preprocessing requires the knowledge of parameters related to the acquisition system, also estimated in our approach. In a second step, we propose an original statistical patch-based framework for noise reduction and preservation of space-time discontinuities. In our study, discontinuities are related to small moving spots with high velocity observed in fluorescence video-microscopy. The idea is to minimize an objective nonlocal energy functional involving spatio-temporal image patches. The minimizer has a simple form and is defined as the weighted average of input data taken in spatially-varying neighborhoods. The size of each neighborhood is optimized to improve the performance of the pointwise estimator. The performance of the algorithm (which requires no motion estimation) is then evaluated on both synthetic and real image sequences using qualitative and quantitative criteria.

Document type :

Journal articles

Domain :

Computer Science [cs] / Image Processing [eess.IV]

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https://hal.inria.fr/inria-00541082
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Submitted on : Monday, November 29, 2010 - 5:38:34 PM
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