A Workingperson’s Guide to Deconvolution in Light Microscopy
Author(s) -
Wes Wallace,
Lutz H. Schaefer,
Jason R. Swedlow
Publication year - 2001
Publication title -
biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/01315bi01
Subject(s) - deconvolution , microscope , fluorescence microscope , microscopy , blind deconvolution , computer science , focus (optics) , process (computing) , fluorescence lifetime imaging microscopy , point spread function , fluorescence , optics , artificial intelligence , physics , algorithm , operating system
Thefluorescence microscope is routinely used to study cellular structure in many biomedical research laboratories and is increasingly used as a quantitative assay system for cellular dynamics. One of the major causes of image degradation in the fluorescence microscope is blurring. Deconvolution algorithms use a model of the microscope imaging process to either subtract or reassign out-of-focus blur. A variety of algorithms are now commercially available, each with its own characteristic advantages and disadvantages. In this article, we review the imaging process in the fluorescence microscope and then discuss how the various deconvolution methods work. Finally, we provide a summary of practical tips for using deconvolution and discuss imaging artifacts and how to minimize them.
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