Deconvolution of confocal microscopy images using proximal iteration and sparse representations

Details Deconvolution of confocal microscopy images using proximal iteration and sparse representations Deconvolution of confocal microscopy images using proximal iteration and sparse representations

2008-03-18

arxiv.org/abs/0803.2622v2

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

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Scientific paper

10.1109/ISBI.2008.4541101

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 \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.

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