Deconvolution of confocal microscopy images using proximal iteration and sparse representations

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dc.creatorDupé, François-Xavier
dc.creatorFadili, Jalal
dc.creatorStarck, Jean Luc
dc.date2008-03-18
dc.date2008-06-13
dc.date.accessioned2026-07-07T12:17:45Z
dc.date.available2026-07-07T12:17:45Z
dc.descriptionWe propose a deconvolution algorithm for images blurred and degraded by a Poisson noise. The algorithm uses a fast proximal backward-forward splitting iteration. This iteration minimizes an energy which combines a \textit{non-linear} data fidelity term, adapted to Poisson noise, and a non-smooth sparsity-promoting regularization (e.g $\ell_1$-norm) over the image representation coefficients in some dictionary of transforms (e.g. wavelets, curvelets). Our results on simulated microscopy images of neurons and cells are confronted to some state-of-the-art algorithms. They show that our approach is very competitive, and as expected, the importance of the non-linearity due to Poisson noise is more salient at low and medium intensities. Finally an experiment on real fluorescent confocal microscopy data is reported.
dc.identifierhttps://arxiv.org/abs/0803.2622
dc.identifierhttp://arxiv.org/abs/0803.2622
dc.identifierBiomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on, Paris : France (2008)
dc.identifierdoi:10.1109/ISBI.2008.4541101
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212179
dc.subjectApplications
dc.subjectOptimization and Control
dc.subjectStatistics Theory
dc.titleDeconvolution of confocal microscopy images using proximal iteration and sparse representations
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