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Deconvolution of Confocal Microscopy Images Using Proximal Iteration and Sparse Representations
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fadilia, M. J. Starckb, J.-L. |
| Copyright Year | 2008 |
| Abstract | 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://vision.lbl.gov/People/han/isbi2008/pdfs/0000736.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
