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Noise that has a frequency spectrum of predominantly zero power level over all frequencies except for a few narrow bands or spikes. Note: An example of black noise in a facsimile transmission system is the spectrum that might be obtained when scanning a black area in which there are a few random white spots. Thus, in the time domain, a few ...
Grey noise spectrum The result is that grey noise contains all frequencies with equal loudness , as opposed to white noise , which contains all frequencies with equal energy . The difference between the two is the result of psychoacoustics , more specifically the fact that the human hearing is more sensitive to some frequencies than others.
In signal processing theory, Gaussian noise, named after Carl Friedrich Gauss, is a kind of signal noise that has a probability density function (pdf) equal to that of the normal distribution (which is also known as the Gaussian distribution). [1] [2] In other words, the values that the noise can take are Gaussian-distributed.
Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory .
A gray-scale photography with different signal-to-noise ratios (SNRs). The SNR values are given for the rectangular region on the forehead. The plots at the bottom show the signal intensity in the indicated row of the image (red: original signal, blue: with noise).
Different types of noise are generated by different devices and different processes. Thermal noise is unavoidable at non-zero temperature (see fluctuation-dissipation theorem), while other types depend mostly on device type (such as shot noise, [1] [3] which needs a steep potential barrier) or manufacturing quality and semiconductor defects, such as conductance fluctuations, including 1/f noise.
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Noise in computer graphics refers to various pseudo-random functions used to create textures, including: Gradient noise, created by interpolation of a lattice of pseudorandom gradients Perlin noise, a type of gradient noise developed in 1983; Simplex noise, a method for constructing an n-dimensional noise function comparable to Perlin noise