Deconvolution is an image processing technique that suppresses image blur resulting from the passage of light through the optical path of a microscope. Every optical path can be described by a so called point spread function (PSF) which specifies how a single point of the image will look like when captured by a camera.
There are the following deconvolution methods available in the deconvolution modules all of which can be applied to either 2D or 3D (including Z-dimension) image documents:
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AutoQuant Real-Time 2D Deconvolution
RT Deconvolution allows the user to observe live specimens with less out-of-focus blur. It allows faint biological processes to be observed that may otherwise be missed and increases observed signal-to-noise ratio. The algorithm enables high speed processing and therefore is very suitable for use on Live signal from camera. Real-time deconvolution can be applied to Live image continuously or just at capture-time. It can be also used for processing saved images.
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AutoQuant Blind Deconvolution
This algorithm deconvolves Z stack images when the PSF is not known exactly. Some characteristics of the optical path shall be known in order for the deconvolution module to work properly.
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Deconvolution (Standard)
The mathematical algorithm deconvolve the image when all input PSF parameters are known accurately.
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Blind Deconvolution
This mathematical algorithm deconvolve the image even when the PSF is not known exactly, but only a rough estimation of the PSF parameters is available. The algorithm improves the PSF/source image estimations iteratively during the deconvolution process.
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Fast (Wiener) Deconvolution
Compared to the two previous iterative methods, this algorithm takes a fraction of the computing time. The input PSF parameters have to be known accurately.