# Gaussian noise

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## Gaussian noise

[¦gau̇·sē·ən ′nȯiz]
(communications)
Noise that has a frequency distribution which follows the Gaussian curve.
(mathematics)

## Gaussian noise

(1) In communications, a random interference generated by the movement of electricity in the line. It is similar to white noise, but confined to a narrower range of frequencies. You can actually see and hear Gaussian noise when you tune your TV to a channel that is not operating. Contrast with white noise and pink noise. See Gaussian distribution and Gaussian blur.

(2) A random distribution of artifacts in analog video images that makes everything look soft and slightly blurry. On close inspection, one can see tiny specks in random patterns. Found on films shot with older cameras as well as films and videotapes that have been archived for a long time, dynamic noise reduction (DNR) circuits can eliminate much of the Gaussian noise when the analog material is converted to digital. See dynamic noise reduction.
References in periodicals archive ?
9861 Table 2: Performance of Proposed Algorithm for Multiple Standard Watermark Images under JPEG Compression Attack and Gaussian Noise Addition Attack.
Where F(n) is used as a white Gaussian noise with zero-mean and unity variance independent with the plant internal noise.
Figure 12(b) shows that, in JAFFE database, with the increase of Gaussian noise variance, SRC's SCI falls faster than the method described in this paper.
Caption: Figure 10: The overall results for the irin011 image with noise: (a) total curves for the images corrupted by Gaussian noise of different variances, (b) total curves for the images corrupted by Salt and pepper noise of different intensities.
Artificial Gaussian noise is added on three virtual hand models and the images are denoised using OMP-based K-SVD and NAOMP-based K-SVD, both without filter preprocessing.
Here we first present a minimal review on signal detection and describe optimal statistics and filter under Gaussian noise.
The additive zero-mean white noise models we considered are the Gaussian noise (AWGN) defined in (4.
where r(t) = s(t) + n(t), s(t) is signal, n(t) the Gaussian noise, and s(t) and n(t) are independently distributed.
c] the carrier frequency, and e(t) the Gaussian noise.
They are also used in systems theory in connection with nonlinear operations on Gaussian noise.
It is common to derive signal-processing algorithms using the additive white Gaussian noise assumption.
Furthermore, in order to simulate realistic scenarios, Gaussian noise is added into the received signals.

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