Difference between revisions of "Convolution Theorem"

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===Short Topical Videos===
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* [http://www.youtube.com/watch?v=a0IdGLczoAA The Convolution Theorem] by Aaron Parsons
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* [http://www.khanacademy.org/video/introduction-to-the-convolution?playlist=Differential%20Equations Introduction to the Convolution] by Khan Academy
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===Reference Material===
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* [http://www.fourier-series.com/Convolution/conv-integral.html Convolution Integral (fourier-series.com, Brent Locher)]
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* [http://www.fourier-series.com/fourierseries2/convolution.html Digital Convolution (fourier-series.com, Brent Locher)]
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<latex>
 
<latex>
 
\documentclass[]{article}
 
\documentclass[]{article}
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\section*{Convolution Theorem}
 
\section*{Convolution Theorem}
  
The {\it convolution} is a useful operation with applications ranging from photo editing (blurring) to
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The {\it convolution} is a useful operation with applications ranging from photo editing to
crystallography to astronomy.
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crystallography to astronomy.  In words, the convolution of two functions $f,g$ is what you get when you smooth one function ($f$) by another ($g$).  Note that the order of $f$ and $g$ does not matter, though people often call the latter the ``kernel''.  Smoothing $f$ by $g$ means that you slide $g$ along $f$, and at each step along the way, you sum up all of the parts of $f$ with weights drawn from the value of $g$ at the point you slid it to.  In essence, you are blurring $f$ by $g$.
  
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Mathematically, this is described as: 
  
 
\begin{align}
 
\begin{align}
[f*g](\tau)&\equiv \int{f(t)g(\tau-t)dt}\\
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\left[f*g\right](\tau)&\equiv \int{f(t)g(\tau-t)dt}\\
 
& = \frac1{(2\pi)^2}\int\!\!\!\int{\hat f(\omega_1)e^{i\omega_1 t}d\omega_1\,
 
& = \frac1{(2\pi)^2}\int\!\!\!\int{\hat f(\omega_1)e^{i\omega_1 t}d\omega_1\,
 
                 \hat g(\omega_2)e^{i\omega_2(\tau-t)}d\omega_2\,dt}\\
 
                 \hat g(\omega_2)e^{i\omega_2(\tau-t)}d\omega_2\,dt}\\

Latest revision as of 16:32, 15 October 2012

Short Topical Videos[edit]

Reference Material[edit]


Convolution Theorem

Fourier Transform

Here are the definitions we will use for the forward () and inverse () Fourier transforms:

where is the angular frequency coordinate that is the Fourier complement of time , and a top-hat is generally used to denote Fourier-domain quantities.

Convolution Theorem

The convolution is a useful operation with applications ranging from photo editing to crystallography to astronomy. In words, the convolution of two functions is what you get when you smooth one function () by another (). Note that the order of and does not matter, though people often call the latter the “kernel”. Smoothing by means that you slide along , and at each step along the way, you sum up all of the parts of with weights drawn from the value of at the point you slid it to. In essence, you are blurring by .

Mathematically, this is described as:

Renaming to be (which we are totally free to do), we get a statement of the convolution theorem:

Convolution vs. Correlation

Correlation is very similar to convolution, and it is best defined through its equivalent “correlation theorem”:

The difference between correlation and convolution is that that when correlating two signals, the Fourier transform of the second function ( in equation None) is conjugated before multiplying and integrating. Using that

we can show that correlating and is equivalent to convolving with a conjugated, time-reversed version of :

Although this relation between convolution and correlation is often mentioned in the literature, I don’t personally find it very intuitively illuminating. I much prefer the “correlation theorem” in equation (None), because when it is combined with the expression of a time-shifted signal in Fourier domain:

it shows that correlating a flat-spectrum signal with a time-shifted version of itself yields a measure of the power of the signal at the delay corresponding to the time shift: