موجک هار سری خاصی از توابع است که اکنون به عنوان اولین موجک شناخته می‌شود. این سری اولین بار توسط آلفرد هار، ریاضیدان مجاری در سال ۱۹۰۹ پیشنهاد شد^[۱]. موجک هار ساده‌ترین موجک ممکن می‌باشد. مشکل این موجک این است که پیوسته نیست و در نتیجه مشتق‌پذیر نمی‌باشد. موجک مادر هار به شکل زیر تعریف می‌شود:

و تابع مقیاس‌کننده نیز برابر است با:

In continuous wavelet transforms, a given signal of finite energy is projected on a continuous family of frequency bands (or similar subspaces of the L^p function space L²(R) ). For instance the signal may be represented on every frequency band of the form [f, 2f] for all positive frequencies f > 0. Then, the original signal can be reconstructed by a suitable integration over all the resulting frequency components.

The frequency bands or subspaces (sub-bands) are scaled versions of a subspace at scale 1. This subspace in turn is in most situations generated by the shifts of one generating function ψ in L²(R), the mother wavelet. For the example of the scale one frequency band [1, 2] this function is

with the (normalized) sinc function. That, Meyer's, and two other examples of mother wavelets are:

Meyer

Morlet

Mexican Hat

The subspace of scale a or frequency band [1/a, 2/a] is generated by the functions (sometimes called child wavelets)

where a is positive and defines the scale and b is any real number and defines the shift. The pair (a, b) defines a point in the right halfplane R₊ × R.

The projection of a function x onto the subspace of scale a then has the form

with wavelet coefficients

See a list of some Continuous wavelets.

For the analysis of the signal x, one can assemble the wavelet coefficients into a scaleogram of the signal.

A wavelet is a wave-like oscillation with an amplitude that begins at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor. Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing. Wavelets can be combined, using a "reverse, shift, multiply and integrate" technique called convolution, with portions of a known signal to extract information from the unknown signal.

Seismic Wavelet

For example, a wavelet could be created to have a frequency of Middle C and a short duration of roughly a 32nd note. If this wavelet was to be convolved with a signal created from the recording of a song, then the resulting signal would be useful for determining when the Middle C note was being played in the song. Mathematically, the wavelet will resonate if the unknown signal contains information of similar frequency – just as a tuning fork physically resonates with sound waves of its specific tuning frequency. This concept of resonance is at the core of many practical applications of wavelet theory.

As a mathematical tool, wavelets can be used to extract information from many different kinds of data, including – but certainly not limited to – audio signals and images. Sets of wavelets are generally needed to analyze data fully. A set of "complementary" wavelets will decompose data without gaps or overlap so that the decomposition process is mathematically reversible. Thus, sets of complementary wavelets are useful in wavelet based compression/decompression algorithms where it is desirable to recover the original information with minimal loss.

In formal terms, this representation is a wavelet series representation of a square-integrable function with respect to either a complete, orthonormal set of basis functions, or an overcomplete set or frame of a vector space, for the Hilbert space of square integrable functions.

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