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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Pankajakshan, P. Bo Zhang Blanc-Feraud, L. Kam, Z. Olivo-Marin, J.-C. Zerubia, J. |
| Copyright Year | 2008 |
| Description | Author affiliation: INRIA/CNRS/UNSA, Sophia Antipolis (Pankajakshan, P.; Blanc-Feraud, L.; Zerubia, J.) |
| Abstract | Optical Sections of biological samples obtained from a fluorescence Confocal Laser Scanning Microscopes (CLSM) are often degraded by out-of-focus blur and photon counting noise. Such physical constraints on the observation are a result of the diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier respectively. Hence, the image stacks can benefit from postprocessing restoration methods based on deconvolution. The parameters of the acquisition system's Point Spread Function (PSF) may vary during the course of experimentation, and so they have to be estimated directly from the observation data. We describe here an alternate minimization algorithm for the simultaneous blind estimation of the specimen 3D distribution of fluorescent sources and the PSF. Experimental results on real data show that the algorithm provides very good de- convolution results in comparison to theoretical microscope PSF models. |
| Starting Page | 740 |
| Ending Page | 743 |
| File Size | 492206 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424420025 |
| DOI | 10.1109/ISBI.2008.4541102 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-05-14 |
| Publisher Place | France |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Deconvolution Fluorescence Optical noise Optical diffraction Biomedical optical imaging Optical microscopy Laser noise Laser theory Degradation Photomultipliers blind deconvolution confocal microscopy point spread function Richardson-Lucy algorithm total variation regularization |
| Content Type | Text |
| Resource Type | Article |
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