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Wavelet and multiresolution process

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Wavelet and multiresolution process

Pei Wu

5.Nov 2012

Open set: any point A in the set must have a open ball O(r,A) contained in the set.

Closed set: complement of open set.

Intersection of closed set is always closed. Union of open set is always open

Compact: if we put infinite point in the set it must have infinity point “gather” around some point in the set.

Complete: a “converge” sequence must converge at a point in the set.

- Hilbert space is a space…
- linear
- complete
- with norm
- with inner product

- Example: Euclidean space, L2 space, …

f,g is orthogonal iff <f,g>=0

f is normalized iff <f,f>=1

Orthonormal basis: e1, e2, e3,… s.t.

a set of basis is called complete if

A set of element {ei} is orthonormal if and only if:

A orthonormal set induces isometric mapping between Hilbert space and l2.

in context of Fourier transform we suppose the frequency spectrum is invariant across time:

However in many cases we want:

A function cannot be localized in both time and frequency (uncertainty principle).

High frequency resolution means low time resolving power.

Use big ruler to measure big thing, small ruler to measure small thing.

Use scale transform to construct ruler with different resolution.

a,b take only discrete number:

And we want them to be orthogonal:

(a)Meyer

(b,c)Battle-Lemarie

(d) Haar

(e,f)Daubechies

Multiresolution analysis

A series of linear subspace {Vi} that:

Firstly we find a set of orthonormal basis in V0:

hn would play important role in discrete analysis

is linearly independent but not orthogonal.

is orthonormal basis of V0

Use spline to get continuous function

Mallat algorithm : top-down

Given c1 how can we get c0 and d0?

Given c0 and d0 how to reconstruct c1 ?

Subband coding

Wavelet expansion of 2D function

Basis for 2D function:

We can carry on decomposition on high-frequency part

Adaptive approach to decide decompose or not.

Thank You!!