Deconvolution and Denoising for Confocal Microscopy

Praveen Pankajakshan (), Gilbert Engler (), Laure Blanc-Féraud () and Josiane Zerubia ()
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Praveen Pankajakshan: Institut Pasteur
Gilbert Engler: INRA
Laure Blanc-Féraud: I3S (CNRS/UNS) UMR 7271 CNRS/UNSA and Inria, Algorithmes/Euclide-B
Josiane Zerubia: Ariana project-team, Inria Sophia Antipolis Méditerranée

Chapter Chapter 4 in Modeling in Computational Biology and Biomedicine, 2013, pp 117-163 from Springer

Abstract: Abstract Fluorescence light microscopes, especially the confocal laser scanning microscopes, have become a powerful tool in life sciences for observing biological samples in order to detect the distribution of proteins or other molecules of interest. Using this tool, biologists can study cells and their sub-cellular structures, identify, and precisely localize cellular organelles and supra-molecular structures. The confocal microscope is a type of fluorescent light microscope that gives very good two-dimensional optical sections of three-dimensional specimens, rejects the background auto-fluorescence, and offers a good contrast. However, there are some inherent limitations in confocal images such as the blurring effects due to the diffraction limit of the optics, and the low signal levels. The aim of this chapter is to introduce the reader to the basics of the light and confocal microscopes, their imaging limitations, and the mathematics involved in the resolution and signal-to-noise ratio improvement methodologies. Although user-friendly restoration software packages are available in the market, image restoration by deconvolution remains a difficult task for many microscopist and choosing the right software is often a case of trial and error due to a lack of knowledge of the applied algorithm. It is with the objective of resolving this issue that the most recent developments are intuitively explained, with some concrete examples to explain the underlying principles. The current open problems in the field of microscopy and methodological challenges are mentioned towards the end of the chapter.

Keywords: Confocal Laser Scanning Microscope; Graphical Processing Unit; Blind Deconvolution; Deconvolution Algorithm; Total Variation Regularization (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-31208-3_4

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