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In mathematics, deconvolution is an algorithm-based process used to reverse the effects of convolution on recorded data. The concept of deconvolution is widely used in the techniques of signal processing and image processing. Because these techniques are in turn widely used in many scientific and engineering disciplines, deconvolution finds many applications.
In general, the objective of deconvolution is to find the solution of a convolution equation of the form:
f
∗
g
=
h
{\\displaystyle f*g=h\\,}
Usually, h is some recorded signal, and f is some signal that we wish to recover, but has been convolved with some other signal g before we recorded it. The function g might represent the transfer function of an instrument or a driving force that was applied to a physical system. If we know g, or at least know the form of g, then we can perform deterministic deconvolution. However, if we do not know g in advance, then we need to estimate it. This is most often done using methods of statistical estimation.
In physical measurements, the situation is usually closer to
(
f
∗
g
)
+
ε
=
h
{\\displaystyle (f*g)+\\varepsilon =h\\,}
In this case ε is noise that has entered our recorded signal. If we assume that a noisy signal or image is noiseless when we try to make a statistical estimate of g, our estimate will be incorrect. In turn, our estimate of ƒ will also be incorrect. The lower the signal-to-noise ratio, the worse our estimate of the deconvolved signal will be. That is the reason why inverse filtering the signal is usually not a good solution. However, if we have at least some knowledge of the type of noise in the data (for example, white noise), we may be able to improve the estimate of ƒ through techniques such as Wiener deconvolution.
Deconvolution is usually performed by computing the Fourier Transform of the recorded signal h and the transfer function g, apply deconvolution in the Frequency domain, which in the case of absence of noise is merely:
F
=
H
/
G
{\\displaystyle F=H/G\\,}
F, G, and H being the Fourier Transforms of f, g, and h respectively. Finally inverse Fourier Transform F to find the estimated deconvolved signal f.
The foundations for deconvolution and time-series analysis were largely laid by Norbert Wiener of the Massachusetts Institute of Technology in his book Extrapolation, Interpolation, and Smoothing of Stationary Time Series (1949). The book was based on work Wiener had done during World War II but that had been classified at the time. Some of the early attempts to apply these theories were in the fields of weather forecasting and economics.
After the device has made on the order of 8,000 measurements, the researchers have to do their own flip, this a mathematical one called deconvolution, to process the data and fit it into a three-dimensional shape.
There are several which will yield basic info such as radius, some measure of limb darkening, and some measure of gross shape – speckle interferometry, intensity interferometry, deconvolution of eclipse light curves (in eclipsing binaries), stellar occultations (by the lunar limb), more?
On p 4066 you will find fig 12 c-d deconvolution for the size distributions of magnetites from ALH84001 and terrestrial MV-1 magnetotactic bacteria.
Using specialized deconvolution software, the image in that bell is obviously from The Ring Those that read this blog shall be cursed.
They are well separated, show a nice gaussian shape, and deconvolution based on just the chromatographic domain will likely already work.
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