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Deconvolution of confocal microscopy images using proximal iteration and sparse representations

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3 Author(s)
Dupe, F.-X. ; GREYC UMR CNRS, Caen ; Fadili, M.J. ; Starck, J.L.

We 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 non-linear data fidelity term, adapted to Poisson noise, and a non- smooth sparsity-promoting regularization (e.g lscr1-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.

Published in:

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Date of Conference:

14-17 May 2008

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