MATH 414 Lecture 28

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Haar Multi-Resolution Analysis (MRA)

  • Vj={fL2f is constant on 2jkx<2j(k+1)k}
  • ϕ(x)={10x<10otherwise

Properties

Nesting

Vj1VjVj+1

Note: V0, V1 — there are functions in V1 that are not in V0.

Density

Separation

Scaling

f(x)Vj if and only if f(2jx)V0

Orthonormal Basis

{2j2ϕ(2jxk)}k= is an orthonormal basis for Vj



Haar Function Decomposition

Two Bases

Vj, {ϕ(2jxk)}k= is orthogonal

fjVj can be defined as fj(x)=k=akjϕ(2jxk)

(where)

Haar: akj=2jϕ(2jxk)f(x)dx


We know Vj1Vj. What is fj1:=projVj1fj?

fj1(x)=k=akj1ϕ(2j1xk)

Question: How are the akj's and akj1's related?

Theorem. akj1 is the average of its "double" term and the odd that follows it:

akj1=12(a2kj+a2k+1j)

Proof. In the arbitary sense, projection in any vector space is given by:

projVf=k=f,ukuk

ajJ1=2j1ϕ(2J1xk)fj(x)dx

In this case, uk=2J12ϕ(2J1xk)

Recall the scaling relation: ϕ(x)=ϕ(2x)+ϕ(2x1)

ϕ(2j1xk)=ϕ(2(2j1xk))+ϕ(2(2j1xk)1)ϕ(2j1xk)=ϕ(2jx2k)+ϕ(2jx2k1)

Plugging this back into our integral gives

akj1=212jfj(x)(ϕ(2jx2k)+ϕ(2jx2k1))dx=12(2jfj(x)ϕ(2jx2k)dx+2jfj(x)ϕ(2jx2k1)dx)=12(a2kj+a2k+1j)

quod erat demonstrandum


Example

f(x)={10x<14414x<12212x<34334x<10otherwise

What's f1?

  • j=1
  • a02=1
  • a12=4
  • a22=2
  • a32=3
  • ak2=0 for k1 or k4

Hence

  • if k1, then ak1=0
  • if k=0, then a01=12(1+4)=32
  • If k=1, then a11=12(23)=12
  • if k2, then ak1=0

Then the function f2 is given by f2=32ϕ(2x)12ϕ(2x1)

f1 is just the projection of f2 onto the space V1



Wavelet Spaces

VjVj+1 and VjVj+1

We saw that ,given fj+1, we can find fj:=projVj(fj+1)

We know wj:=fj+1projVj(fj+1) is orthogonal to Vj (it cannot possibly be in Vj unless wj=0)


What is wj?

w is an open secret... it stands for "wavelet"

Let Wj=VjVj+1={wVj+1wfjfjVj}


What is a basis for Wj?

We'll work with V1, V0, W0

f1(x)=2ϕ(2x)

this means that a01=2

projV0(f1)=k=ak0ϕ(xk)

ak0=a2k+a2k+12={a00=2(12+02)=1a10=a2+a32=0ak0=0k0

f0=ϕ(x)

w0=f1f0=2ϕ(2x)ϕ(x), BUT ϕ(x)=ϕ(2x)+ϕ(2x1). Therefore wj=2ϕ(2x)ϕ(2x)ϕ(2x1)=ϕ(2x)ϕ(2x1)

Now w0V1 and ∉V0.

This is our wavelet ψ(x)! (thunderous applause)

ψ(x)={10x<12112x<10otherwise

We can show that {212ψ(2jxk)}k= is an orthonormal basis for Wj.

wjWj, then

wj(x)=k=bkjψ(2jxk)bkj=2jψ(2jxk)dx

where bkj is the level.


Theorem. Vj1Vj, Vj=Vj1Wj1

If fjVj, then

projWj1(fj)=k=bkj1ψ(2j1xk)bkj1=12(a2kja2k+1j)akj1=12(a2kj+a2k+1j)

bkj1 gives the details, and akj1 provides smoothing.

Proof. [omitted].

quod erat demonstrandum