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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...
We present a non-parametric regression method for denoising fluorescence video-microscopy volume sequences. The designed method aims at using the 3D+t information in order to restore acquired data contaminated by Poisson and Gaussian noise. We propose to use a variance stabilization transform to deal with the combination of Poisson and Gaussian noise. Consequently, we further propose an adaptive patch-based...
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