In mathematics, the Morlet wavelet (or Gabor wavelet) is a wavelet composed of a complex exponential (carrier) multiplied by a Gaussian window (envelope). This wavelet is closely related to human perception, both hearing and vision.

In 1946, physicist Dennis Gabor, applying ideas from quantum physics, introduced the use of Gaussian-windowed sinusoids for time-frequency decomposition, which he referred to as atoms, and which provide the best trade-off between spatial and frequency resolution. These are used in the Gabor transform, a type of short-time Fourier transform. In 1984, Jean Morlet introduced Gabor's work to the seismology community and, with Goupillaud and Grossmann, modified it to keep the same wavelet shape over equal octave intervals, resulting in the first formalization of the continuous wavelet transform.

The wavelet is defined as a constant ${\displaystyle \kappa _{\sigma }}$ subtracted from a plane wave and then localised by a Gaussian window:

where ${\displaystyle \kappa _{\sigma }=e^{-{\frac {1}{2}}\sigma ^{2}}}$ is defined by the admissibility criterion, and the normalisation constant ${\displaystyle c_{\sigma }}$ is:

The Fourier transform of the Morlet wavelet is:

The "central frequency" ${\displaystyle \omega _{\Psi }}$ is the position of the global maximum of ${\displaystyle {\hat {\Psi }}_{\sigma }(\omega )}$ which, in this case, is given by the positive solution to:

which can be solved by a fixed-point iteration starting at ${\displaystyle \omega _{\Psi }=\sigma }$ (the fixed-point iterations converge to the unique positive solution for any initial ${\displaystyle \omega _{\Psi }>0}$).^{[citation needed]}

The parameter ${\displaystyle \sigma }$ in the Morlet wavelet allows trade between time and frequency resolutions. Conventionally, the restriction ${\displaystyle \sigma >5}$ is used to avoid problems with the Morlet wavelet at low ${\displaystyle \sigma }$ (high temporal resolution).^{[citation needed]}