Deconvolution of confocal microscopy images using proximal iteration and sparse representations

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 nonsmooth sparsity-promoting regularization (e.g 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.

• Type: Conference
• Year: 2008
• Where: ISBI
• Authors: François-Xavier Dupé, Mohamed-Jalal Fadili, Jean-Luc Starck
• Keywords: Backward-forward Splitting Iteration, Fluorescent Confocal Microscopy, ISBI 2008, Medical Imaging, Poisson Noise