Patch-based nonlocal functional for denoising fluorescence microscopy image sequences
IEEE Trans. Med. Imaging. 2010-02-01; 29(2): 442-454
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1. IEEE Trans Med Imaging. 2010 Feb;29(2):442-54. doi: 10.1109/TMI.2009.2033991.
Epub 2009 Nov 6.
Patch-based nonlocal functional for denoising fluorescence microscopy image
Boulanger J(1), Kervrann C, Bouthemy P, Elbau P, Sibarita JB, Salamero J.
(1)Radon Institute for Computational and Applied Mathematics, 4040 Linz, Austria.
Nat Methods. 2010 Oct;7(10):782.
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.
PMID: 19900849 [Indexed for MEDLINE]