Noise modeling for intensified camera in fluorescence imaging: application to image denoising

Abstract : 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 to local noise statistics. Performances are demonstrated on real samples on both widefield and confocal imaging combined with frequency domain fluorescence lifetime imaging (FD FLIM).
Type de document :
Communication dans un congrès
Parvin, Bahram. ISBI - IEEE International Symposium on Biomedical Imaging, Apr 2013, San-Francisco, United States. pp.600-603, 2013, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6556546&tag=1〉. 〈10.1109/ISBI.2013.6556546〉
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Contributeur : Charles Kervrann <>
Soumis le : mercredi 15 janvier 2014 - 11:42:46
Dernière modification le : vendredi 31 août 2018 - 09:18:24

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Philippe Roudot, Charles Kervrann, Jérôme Boulanger, François Waharte. Noise modeling for intensified camera in fluorescence imaging: application to image denoising. Parvin, Bahram. ISBI - IEEE International Symposium on Biomedical Imaging, Apr 2013, San-Francisco, United States. pp.600-603, 2013, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6556546&tag=1〉. 〈10.1109/ISBI.2013.6556546〉. 〈hal-00931360〉

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