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In this paper, we present a stochastic NL-means-based de-noising algorithm for generalized non-parametric noise models. First, we provide a statistical interpretation to current patch-based neighborhood filters and justify the Bayesian inference that needs to explicitly accounts for discrepancies between the model and the data. Furthermore, we investigate the Approximate Bayesian Computation (ABC)...
We propose a statistical framework for noise variance estimation in the case of experimental microscopic images enhanced by an image intensifier. Instrumentally induced noise is modeled and corrected to cope with optical aberrations. In this paper, the spatially varying noise is exploited for denoising applications. Our approach does not need variance stabilization since the algorithm is able to adapt...
We propose a robust statistical framework for reconstructing lifetime map corrupted by vesicle motion in frequency domain FLIM imaging. Instrumental noise is taken into account to improve lifetime estimation. Robust M-estimators and ML-estimators allow to jointly estimate motion and lifetime. Performances are demonstrated on simulated and real samples.
We propose a robust statistical framework for correcting the movements of vesicles in frequency domain FLIM imaging. Movement and lifetime are jointly estimated in a three-step procedure. Robust M-estimators are mainly used to improve accuracy in temporally-varying noisy images. The performance of the proposed method is demonstrated on both simulated and real samples.
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