In signal processing , white noise is a random signal having equal intensity at different frequencies , giving it a constant power spectral density . [1] The term is used, with this or similar meanings, in many scientific and technical disciplines, including physics , acoustic engineering , telecommunications , and statistical forecasting . White noise refers to a statistical model for signals and signal sources, rather than to any specific signal. White noise draws its name from white light , [2] although light that appears white generally does not have a flat power spectral density over the visible band.

In discrete time , white noise is a discrete signal whose samples are regarded as a sequence of serially uncorrelated random variables with zero mean and finite variance ; a single realization of white noise is a random shock . Depending on the context, one may also require that the samples be independent and have identical probability distribution (in other words independent and identically distributed random variables are the simplest representation of white noise). [3] In particular, if each sample has a normal distribution with zero mean, the signal is said to be Gaussian white noise . [4]

The samples of a white noise signal may be sequential in time, or arranged along one or more spatial dimensions. In digital image processing , the pixels of a * white noise image* are typically arranged in a rectangular grid, and are assumed to be independent random variables with uniform probability distribution over some interval. The concept can be defined also for signals spread over more complicated domains, such as a sphere or a torus .

In signal processing , white noise is a random signal having equal intensity at different frequencies , giving it a constant power spectral density . [1] The term is used, with this or similar meanings, in many scientific and technical disciplines, including physics , acoustic engineering , telecommunications , and statistical forecasting . White noise refers to a statistical model for signals and signal sources, rather than to any specific signal. White noise draws its name from white light , [2] although light that appears white generally does not have a flat power spectral density over the visible band.

In discrete time , white noise is a discrete signal whose samples are regarded as a sequence of serially uncorrelated random variables with zero mean and finite variance ; a single realization of white noise is a random shock . Depending on the context, one may also require that the samples be independent and have identical probability distribution (in other words independent and identically distributed random variables are the simplest representation of white noise). [3] In particular, if each sample has a normal distribution with zero mean, the signal is said to be Gaussian white noise . [4]

The samples of a white noise signal may be sequential in time, or arranged along one or more spatial dimensions. In digital image processing , the pixels of a * white noise image* are typically arranged in a rectangular grid, and are assumed to be independent random variables with uniform probability distribution over some interval. The concept can be defined also for signals spread over more complicated domains, such as a sphere or a torus .

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