IEEE Trans Image Process - Convex total variation denoising of Poisson fluorescence confocal images with anisotropic filtering.

Tópicos

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Resumo

Fluorescence confocal microscopy (FCM) is now one of the most important tools in biomedicine research. In fact, it makes it possible to accurately study the dynamic processes occurring inside the cell and its nucleus by following the motion of fluorescent molecules over time. Due to the small amount of acquired radiation and the huge optical and electronics amplification, the FCM images are usually corrupted by a severe type of Poisson noise. This noise may be even more damaging when very low intensity incident radiation is used to avoid phototoxicity. In this paper, a Bayesian algorithm is proposed to remove the Poisson intensity dependent noise corrupting the FCM image sequences. The observations are organized in a 3-D tensor where each plane is one of the images acquired along the time of a cell nucleus using the fluorescence loss in photobleaching (FLIP) technique. The method removes simultaneously the noise by considering different spatial and temporal correlations. This is accomplished by using an anisotropic 3-D filter that may be separately tuned in space and in time dimensions. Tests using synthetic and real data are described and presented to illustrate the application of the algorithm. A comparison with several state-of-the-art algorithms is also presented.

Resumo Limpo

fluoresc confoc microscopi fcm now one import tool biomedicin research fact make possibl accur studi dynam process occur insid cell nucleus follow motion fluoresc molecul time due small amount acquir radiat huge optic electron amplif fcm imag usual corrupt sever type poisson nois nois may even damag low intens incid radiat use avoid phototox paper bayesian algorithm propos remov poisson intens depend nois corrupt fcm imag sequenc observ organ d tensor plane one imag acquir along time cell nucleus use fluoresc loss photobleach flip techniqu method remov simultan nois consid differ spatial tempor correl accomplish use anisotrop d filter may separ tune space time dimens test use synthet real data describ present illustr applic algorithm comparison sever stateoftheart algorithm also present

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