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Wavelet transform

Wavelet transform is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Wavelet transform rather than just read about it. In short: In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform.

Wavelet transform — main illustration
Wavelet transform — illustration

Key takeaways

  • Wavelet transform belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Wavelet transform to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Wavelet transform from memory before moving on to harder problems.

Reference excerpt

In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform.

Definition A function ψ ∈ L 2 ( R ) {\displaystyle \psi \,\in \,L^{2}(\mathbb {R} )} is called an orthonormal wavelet if it can be used to define a Hilbert basis, that is, a complete orthonormal system for the Hilbert space of square-integrable functions on the real line. The Hilbert basis is constructed as the family of functions { ψ j k : j , k ∈ Z } {\displaystyle \{\psi _{jk}:\,j,\,k\,\in \,\mathbb {Z} \}} by means of dyadic translations and dilations of ψ {\displaystyle \psi \,} ,

ψ j k ( x ) = 2 j 2 ψ ( 2 j x − k ) , {\displaystyle \psi _{jk}(x)=2^{\frac {j}{2}}\psi \left(2^{j}x-k\right),}

for integers j , k ∈ Z {\displaystyle j,\,k\,\in \,\mathbb {Z} } . If, under the standard inner product on L 2 ( R ) {\displaystyle L^{2}\left(\mathbb {R} \right)} ,

⟨ f , g ⟩ = ∫ − ∞ ∞ f ( x ) g ( x ) ¯ d x , {\displaystyle \langle f,g\rangle =\int _{-\infty }^{\infty }f(x){\overline {g(x)}}dx,}

this family is orthonormal, then it is an orthonormal system:

⟨ ψ j k , ψ l m ⟩ = ∫ − ∞ ∞ ψ j k ( x ) ψ l m ( x ) ¯ d x , = δ j l δ k m , {\displaystyle {\begin{aligned}\langle \psi _{jk},\psi _{lm}\rangle &=\int _{-\infty }^{\infty }\psi _{jk}(x){\overline {\psi _{lm}(x)}}dx,\\&=\delta _{jl}\delta _{km},\end{aligned}}}

where δ j l {\displaystyle \delta _{jl}\,} is the Kronecker delta. Completeness is satisfied if every function f ∈ L 2 ( R ) {\displaystyle f\,\in \,L^{2}\left(\mathbb {R} \right)} may be expanded in the basis as

f ( x ) = ∑ j , k = − ∞ ∞ c j k ψ j k ( x ) {\displaystyle f(x)=\sum _{j,k=-\infty }^{\infty }c_{jk}\psi _{jk}(x)}

with convergence of the series understood to be convergence in norm. Such a representation of f {\displaystyle f} is known as a wavelet series. This implies that an orthonormal wavelet is self-dual. The integral wavelet transform is the integral transform defined as

… excerpt ends here. Continue reading the full article.

Illustrations

Wavelet transform: An example of the 2D discrete wavelet transform that is used in JPEG2000
An example of the 2D discrete wavelet transform that is used in JPEG2000
Wavelet transform illustration
Wavelet transform illustration
Wavelet transform illustration
Wavelet transform illustration

Worked examples

Example 1 — a first encounter with Wavelet transform

Start with the simplest possible case. Write down what Wavelet transform claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Wavelet transform before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Wavelet transform ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Wavelet transform

In research
Wavelet transform appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Wavelet transform in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Wavelet transform is common in secondary-school and first-year university syllabi. It links to neighbouring topics Data compression, Functional analysis, Image compression, so understanding it makes those chapters shorter.
In everyday life
Look for Wavelet transform outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Wavelet transform in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Wavelet transform means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Wavelet transform out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Wavelet transform in simple terms?

In mathematics, a wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet. This article provides a formal, mathematical definition of an orthonormal wavelet and of the integral wavelet transform.

Why does Wavelet transform matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Wavelet transform?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Wavelet transform.

Tags

  • Data compression
  • Functional analysis
  • Image compression
  • Signal processing
  • Wavelets

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