Non-parametric regression for patch-based fluorescence microscopy image sequence denoising

Abstract : We present a non-parametric regression method for denoising 3D image sequences acquired in fluorescence microscopy. The proposed method exploits 3D+time information to improve the signal-to-noise ratio of images corrupted by mixed Poisson-Gaussian noise. A variance stabilization transform is first applied to the image-data to introduce independence between the mean and variance. This pre-processing 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 demonstrated on both synthetic and real image sequences using qualitative and quantitative criteria.
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[Research Report] RR-6651, INRIA. 2008, pp.34
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  • HAL Id : inria-00322321, version 1

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Jérôme Boulanger, Charles Kervrann, Jean Salamero, Jean-Baptiste Sibarita, Patrick Bouthemy. Non-parametric regression for patch-based fluorescence microscopy image sequence denoising. [Research Report] RR-6651, INRIA. 2008, pp.34. 〈inria-00322321〉

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