Denoising of LSFCM images with compensation for the photoblinking/photobleaching effects

Fluorescence confocal microscopy images present a low signal to noise ratio and a time intensity decay due to the so called photoblinking and photobleaching effects. These effects, together with the Poisson multiplicative noise that corrupts the images, make long time biological observation processes very difficult. In this paper a Bayesian denoising algorithm for Poisson data is presented where two different a priori distributions, one in the time domain and the other in the space domain, are used together to regularize the solution. These distributions, used to describe the spatio-temporal correlation among neighboring pixels, along the time, allow to greatly improve the SNR of the denoised solution, mainly, in the last images on the sequence that present worst SNR. In the observation model the photoblinking and photobleaching effects are explicitly taken into account where the theoretical foundations for the corresponding intensity decay model, outlined here, were recently proposed by the authors. Monte Carlo experiments and validation with other models, using synthetic data, are presented to characterize the performance of the algorithm. Also an example with a real data sequence is included to illustrate its application.