Convolution

The Definition

The integral

g(tτ)h(τ)dτ=gh

is called the convolution of g and h. Here the denotes
convolution, it is sometimes represented by a . This is representing the
amount of overlap of function h as g is shifted over it. It in effect
blends one function with another. This crops up a lot in physics and in
statistics.

The Fourier Transform of a Convolution

If F(u) is the Fourier transform of f(x) and G(u) is the Fourier
transform of g(x), multiplying both Fourier transforms together we have

F(u)G(u)=f(x)e2πiuxdxg(x)e2πiuxdx

So that we can rewrite this as a double integral we replace the x with
two dummy variables and rewrite as

F(u)G(u)=f(τ)e2πiuτdτg(υ)e2πiuυdυ

=e2πiu(τ+υ)f(τ)g(υ)dτdυ

Next, making a change of variables so x=τ+υ,dx=dυ

F(u)G(u)=f(τ)g(xτ)dτdx

=ei2πx[f(τ)g(xτ)dτ]dx

=ei2πxf(x)g(x)dx

i.e., F(u)G(u) and f(x)g(x) are Fourier transform pairs. This
result is very important in optical processing, and signal processing in
general. It means a convolution can be calculated by Fourier transforming
both signals, multiplying the result and inverse transforming back. This is
often quicker than calculating the convolution directly and can be performed
using a simple system of lenses and filters.

Properties

Commutativity

fg=gf

Associativity

f(gh)=(fg)h

Distributivity

f(g+h)=(fg)+(fh)

Scalar multiplication

a(fg)=(af)g=f(ag)

where aC

Differential rule

If is a differential operator

(fg)=fg=fg