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Proceedings Paper

Subpixel deconvolution of 3D optical microscope imagery
Author(s): David S.C. Biggs; Chou-Lung Wang; Timothy J. Holmes; Alexey Khodjakov
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Paper Abstract

Optical light microscopy is a predominant modality for imaging living cells, with the maximum resolution typically diffraction limited to approximately 200nm. The objective of this project is to enhance the resolution capabilities of optical light microscopes using image-processing algorithms, to produce super-resolved imagery at a sub-pixel level. The sub-pixel algorithm is based on maximum-likelihood iterative deconvolution of photon-limited data, and reconstructs the image at a finer scale than the pixel limitation of the camera. The software enhances the versatility of light microscopes, and enables the observation of sub-cellular components at a resolution two to three times finer than previously. Adaptive blind deconvolution is used to automatically determine the point spread function from the observed data. The technology also allows camera-binned or sub-sampled (aliased) data to be correctly processed. Initial investigations used computer simulations and 3D imagery from widefield epi-fluorescence light microscopy.

Paper Details

Date Published: 26 October 2004
PDF: 12 pages
Proc. SPIE 5559, Advanced Signal Processing Algorithms, Architectures, and Implementations XIV, (26 October 2004); doi: 10.1117/12.559526
Show Author Affiliations
David S.C. Biggs, AutoQuant Imaging, Inc. (United States)
Chou-Lung Wang, AutoQuant Imaging, Inc. (United States)
Timothy J. Holmes, AutoQuant Imaging, Inc. (United States)
Alexey Khodjakov, New York State Dept. of Health (United States)

Published in SPIE Proceedings Vol. 5559:
Advanced Signal Processing Algorithms, Architectures, and Implementations XIV
Franklin T. Luk, Editor(s)

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