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Author  |
Pankajakshan, Praveen; Zhang, Bo; Blanc-Féraud, Laure; Kam, Zvi; Olivo-Marin, Jean-Christophe; Zerubia, Josiane |
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Title |
Parametric Blind Deconvolution for Confocal Laser Scanning Microscopy |
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Conference Article |
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Year |
2007 |
Publication |
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE |
Abbreviated Journal |
EMBS 2007 |
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Pages |
6531-6534 |
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Keywords |
3D fluorescent source distribution; PSF; alternate minimization algorithm; confocal laser scanning microscopy; fluorescence images; image acquisition physical process; iterative restoration method; optical fluorescence microscope; parametric blind image deconvolution; point spread function; biomedical optical imaging; deconvolution; fluorescence; image reconstruction; image restoration; iterative methods; laser applications in medicine; medical image processing; minimisation; optical microscopy; optical transfer function |
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Abstract |
In this paper, we propose a method for the iterative restoration of fluorescence confocal laser scanning microscopic (CLSM) images and parametric estimation of the acquisition system's point spread function (PSF). The CLSM is an optical fluorescence microscope that scans a specimen in 3D and uses a pinhole to reject most of the out-of-focus light. However, the quality of the images suffers from two basic physical limitations. The diffraction-limited nature of the optical system, and the reduced amount of light detected by the photomultiplier cause blur and photon counting noise respectively. These images can hence benefit from post-processing restoration methods based on deconvolution. An efficient method for parametric blind image deconvolution involves the simultaneous estimation of the specimen 3D distribution of fluorescent sources and the microscope PSF. By using a model for the microscope image acquisition physical process, we reduce the number of free parameters describing the PSF and introduce constraints. The parameters of the PSF may vary during the course of experimentation, and so they have to be estimated directly from the observed data. A priori model of the specimen is further applied to stabilize the alternate minimization algorithm and to converge to the solutions. |
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Publisher |
IEEE |
Place of Publication |
Piscataway, NJ |
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ISSN |
1557-170X |
ISBN |
978-1-4244-0787-3 |
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Conference |
EMBS 2007 |
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Approved |
yes |
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Call Number |
UCF @ kdamkjer @ Pankajakshan_2007 |
Serial |
49 |
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