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Concept# Noise (signal processing)

Summary

In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion.
Sometimes the word is also used to mean signals that are random (unpredictable) and carry no useful information; even if they are not interfering with other signals or may have been introduced intentionally, as in comfort noise.
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.
Signal processing noise can be classified by its statistical properties (sometimes called the "color" of the noise) and by how it modifies the intended signal:
Additive noise, gets added to the intended signal
White noise
Additive white Gaussian noise
Black noise
Gaussian noise
Pink noise or flicker noise, with 1/f power spectrum
Brownian noise, with 1/f2 power spectrum
Contaminated Gaussian noise, whose PDF is a linear mixture of Gaussian PDFs
Power-law noise
Cauchy noise
Multiplicative noise, multiplies or modulates the intended signal
Quantization error, due to conversion from continuous to discrete values
Poisson noise, typical of signals that are rates of discrete events
Shot noise, e.g.

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Noise floor

In signal theory, the noise floor is the measure of the signal created from the sum of all the noise sources and unwanted signals within a measurement system, where noise is defined as any signal other than the one being monitored. In radio communication and electronics, this may include thermal noise, black body, cosmic noise as well as atmospheric noise from distant thunderstorms and similar and any other unwanted man-made signals, sometimes referred to as incidental noise.

Image noise

Image noise is random variation of brightness or color information in s, and is usually an aspect of electronic noise. It can be produced by the and circuitry of a or digital camera. Image noise can also originate in film grain and in the unavoidable shot noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that obscures the desired information. Typically the term “image noise” is used to refer to noise in 2D images, not 3D images.

Background noise

Background noise or ambient noise is any sound other than the sound being monitored (primary sound). Background noise is a form of noise pollution or interference. Background noise is an important concept in setting noise levels. Background noises include environmental noises such as water waves, traffic noise, alarms, extraneous speech, bioacoustic noise from animals, and electrical noise from devices such as refrigerators, air conditioning, power supplies, and motors.

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This paper is devoted to a study of the role of the fluctuations that the eye is subject to, from the point of view of noise-enhanced processing. To this end, a basic model of the retina is considered, namely a regular sampler subject to space and time fluctuations that model the random sampling and the involuntary eye tremor respectively. The filtering that can be done by the photoreceptor is also taken into account and the study focuses on a stochastic model of a natural scene. To quantify the effect of the noise, a coefficient of correlation between the signal acquired by a given photoreceptor and a given point of the scene that the eye is looking at is considered. It is shown both for academic examples and for a more realistic case that the fluctuations which affect the retina can induce noise-enhanced processing effects. The observed effect is then interpreted as a stochastic control of the retina via the random tremor.

20092007

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