By Meet A. MoradiyaOct 30 2018.jpg)
Image Credits: Konstantin Kolosov/shutterstock.com
Optical microscopy is centuries old and yet remains the central technique of much of modern biological research, particularly in cell biology. It offers cell biologists the unique opportunity to examine living samples in conditions similar to those of the native state.
Deconvolution is often called “restoration”; it is a computational technique to improve the contrast and resolution of digital images. One of the main advantages of deconvolution microscopy over conventional wide-field epifluorescence microscopy is that deconvolution software attempts to eliminate or reverse the effects of out of focus light present in microscope images.
Almost any image acquired on a digital fluorescence microscope can be deconvolved; several new applications are being developed to apply deconvolution techniques to transmitted light images collected according to several contrast improvement strategies. Among the most suitable improvement topics by deconvolution are three-dimensional montages constructed from a series of optical sections.
Deconvolution is sometimes described as an alternative to confocal microscopy. In fact, confocal microscopy and wide-field-deconvolution microscopy both works by eliminating blur, but they do so by opposite means.
Confocal microscopy prevents the detection of blur from the focus by placing a pinhole in front of the detector so that most of the light passing through the pinhole comes from the focal plane and not from the surrounding regions. Wide-field microscopy allows blurred light to reach the detector; deconvolution then attempts to subtract blurred light from the image or moved it back to its source.
The confocal microscopy is particularly well fitted for thick specimens like biological tissues, while wide-field-deconvolution microscopy has proven to be a powerful method for imaging low light levels, such as live cells carrying fluorescently labeled proteins and nucleic acids.
Cause of Image Degradation
The image degradation can be divided into four independent phenomena: noise, glare, blur and focus errors. The main task that deconvolution fixes to remove blur. Deconvolution algorithms can and do remove noise, but this is a relatively simple aspect of their overall performance.
Algorithms for Deconvolution Microscopy
Over the last ten years, a wide variety of simple and complex algorithms have been developed to help the microscopist eliminate the blur of digital images. The Scrolling and restoring images are the two classes that used in deconvolution algorithm in optical microscopy.
The deblurring algorithms are basically two-dimensional because they apply a plane-by-plane operation to each two-dimensional plane of a three-dimensional image stack. On the other hand, image restoration algorithms are correctly called "three-dimensional" because they work simultaneously on each pixel of a stack of three-dimensional images.
Artifacts and Aberrations in Deconvolution
Once the deconvolution algorithms have been applied, the restored image may include apparent artifacts such as staining, ringing, or discontinuous staining of the cytoskeleton. In some cases, these problems are related to the representation of the data and will not occur with a different algorithm or software package. Artifacts can also occur when the processing settings are not properly configured for the raw image. Finally, artifacts are often not caused by calculation, but by histology, optical misalignment or electronic noise. When attempting to diagnose the source of an artifact, the first step is to carefully compare the raw image to the deconverted image.
Resolution Criteria for Deconvolution
The resolution in optical microscopy is often evaluated using an optical unit called the Rayleigh criterion, originally formulated to determine the resolution of two-dimensional telescope images, but has since spread into many other areas in optics. The Rayleigh criterion is defined in terms of the minimum distance that can be resolved between two point light sources generated by a sample and does not depend on the magnification used to produce the image. In the deconvolution analysis, the three-dimensional nature of the point spread function must be taken into account when applying the Rayleigh criterion.
Resources
- McNally, J. G., Karpova, T., Cooper, J. and Conchello, J. A. (1999) ‘Three-dimensional imaging by deconvolution microscopy’, Methods: A Companion to Methods in Enzymology, 19(3), pp. 373–385. doi: 10.1006/meth.1999.0873.
- Wallace, W., Schaefer, L. H. and Swedlow, J. R. (2001) ‘A working person’s guide to deconvolution in light microscopy’, Biotechniques, 31(5), pp. 1076–1097. doi: 11730015.
- Shaw, P. J. (2006) ‘Comparison of Widefield Deconvolution and Confocal Microscopy for Three-Dimensional Imaging’, Handbook Of Biological Confocal Microscopy, (Dic), pp. 453–467. doi: 10.1007/978-0-387-45524-2_23.
- https://micro.magnet.fsu.edu/primer/digitalimaging/deconvolution/deconintro.html
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