MATH 414 Lecture 28

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Haar Multi-Resolution Analysis (MRA)

Properties

Nesting

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

Density

Separation

Scaling

if and only if

Orthonormal Basis

is an orthonormal basis for



Haar Function Decomposition

Two Bases

, is orthogonal

can be defined as

(where)

Haar:


We know . What is ?

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

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

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

In this case,

Recall the scaling relation:

Plugging this back into our integral gives

quod erat demonstrandum


Example

What's ?

  • for or

Hence

  • if , then
  • if , then
  • If , then
  • if , then

Then the function is given by

is just the projection of onto the space



Wavelet Spaces

and

We saw that ,given , we can find

We know is orthogonal to (it cannot possibly be in unless )


What is ?

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

Let


What is a basis for ?

We'll work with , ,

this means that

, BUT . Therefore

Now and .

This is our wavelet ! (thunderous applause)

We can show that is an orthonormal basis for .

, then

where is the level.


Theorem. ,

If , then

gives the details, and provides smoothing.

Proof. [omitted].

quod erat demonstrandum