A workingperson's guide to deconvolution in light microscopy.

By W. Wallace, L. Schaefer, Jason R. Swedlow. BioTechniques, 2001

366 citations recorded by the source corpus.

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.

Subfields: Biophysics

View Paper: https://www.semanticscholar.org/paper/464dfac3c27e4c2f2c2982da670b255e2207d911

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In Biophysics

Citations in the corpus

DeconvolutionLab2: An open-source software for deconvolution microscopy. (2017, background): “These practical issues have been well considered in the literature [4,31].”
SINGLE-PARTICLE TRACKING (2009, methodology): “Alternatively one can use deconvolution, a mathematical technique in which light from outside the focal plane is subtracted or reassigned to its original source plane [100].”
Neuron tracing in perspective (2010, background): “Although prevention is better than cure, to some extent artifacts can be removed by image processing operations such as (nonlinear) smoothing (21,22), deconvolution (20,23,24), shading correction (21,22), and morphological filtering (25–27).”
A guided tour into subcellular colocalization analysis in light microscopy (2006, methodology): “Deconvolution may restore the resolution of images in both wide-field and confocal microscopy and is the subject of some excellent reviews (Wallace & Swedlow, 2001; Sibarita, 2005).”
A guide to super-resolution fluorescence microscopy (2010, background): “Computational image restoration assigns out-of-focus intensity back to its originating position in space, resulting in a significantly improved image contrast and a modest increase in spatial resolution (Agard and Sedat, 1983; Agard et al., 1989; Wallace et al., 2001).”