81.147.114.131 at 22:46, 6 July 2013

2013-07-06T22:46:45Z

← Older revision		Revision as of 00:46, 7 July 2013
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	~~Wilber Berryhill~~ is the ~~title his mothers~~ and ~~fathers gave him~~ and ~~he totally digs that name~~. ~~What me~~ and ~~my family adore~~ is ~~doing ballet but I've been taking on new things recently~~. ~~For~~ many ~~years he'~~s ~~been living~~ in ~~Mississippi~~ and ~~he doesn't strategy on altering it~~. ~~Distributing manufacturing is how he makes a living~~.<br><br>~~My site~~ [http://www.~~erozrywka~~.pl/~~users~~/~~GFHD love psychic readings~~]		{{multiple image
			\| direction = vertical
			\| width = 256
			\| footer =
			\| image1 = 512x512-No-Noise.jpg
			\| alt1 = Without noise
			\| caption1 = Without noise
			\| image2 = 512x512-Gaussian-Noise.jpg
			\| alt2 = With Gaussian noise
			\| caption2 = With Gaussian noise
			}}

			'''Gaussian noise''' represents [[statistical noise]] having [[probability density function]] (PDF) equal to that of the [[normal distribution]], which is also known as the [[Gaussian distribution]].<ref name="Barbu" /><ref name="Handbook"/> In other words, the values that the noise can take on are Gaussian-distributed.

			The probability density function <math>p</math> of a Gaussian random variable <math>z</math> is given by:

			: <math>p_G(z) = \frac{1}{\sigma\sqrt{2\pi}} e^{ -\frac{(z-\mu)^2}{2\sigma^2} }</math>

			where <math>z</math> represents the grey level, <math>\mu</math> the [[mean]] value and <math>\sigma</math> the [[standard deviation]].<ref name="Basel" />

			A special case is ''white Gaussian noise'', in which the values at any pair of times are [[iid\|identically distributed]] and [[statistically independent]] (and hence [[uncorrelated]]). In [[Channel (communications)\|communication channel]] testing and modelling, Gaussian noise is used as additive [[white noise]] to generate [[additive white Gaussian noise]].

			In [[telecommunications]] and [[computer networking]], [[Channel (communications)\|communication channels]] can be affected by [[wideband]] Gaussian noise coming from many natural sources, such as the thermal vibrations of atoms in conductors (referred to as [[thermal noise]] or [[Johnson-Nyquist noise]]), [[shot noise]], [[Black body\|black body radiation]] from the earth and other warm objects, and from [[Astronomical object\|celestial sources]] such as the [[Sun]].

			== Gaussian noise in digital images ==

			Principal sources of Gaussian noise in [[digital image]]s arise during acquisition eg. [[sensor noise]] caused by poor illumination and/or high temperature, and/or transmission eg. [[Circuit noise level\|electronic circuit noise]].<ref name="Basel" /> In [[digital image processing]] Gaussian noise can be reduced using a [[spatial filter]], though when smoothing an image, an undesirable outcome may result in the blurring of fine-scaled image edges and details because they also correspond to blocked high frequencies. Conventional spatial filtering techniques for [[Noise reduction\|noise removal]] include: mean ([[convolution]]) filtering, [[median filter\|median filtering]] and [[Gaussian smoothing]].<ref name="Barbu" /><ref name="HIPR2" />

			== See also ==

			* [[Gaussian process]]
			* [[Gaussian smoothing]]

			== References ==

			{{Reflist\|refs=
			<ref name="Handbook">{{cite web \|url=http://www.sfu.ca/sonic-studio/handbook/Gaussian_Noise.html \|title=Handbook for Acoustic Ecology \|edition=Second \|date=1999 \|publisher=Cambridge Street Publishing \|editor=Barry Truax \|accessdate=2012-08-05}}</ref>
			<ref name="HIPR2">{{cite web \| url=http://homepages.inf.ed.ac.uk/rbf/HIPR2/noise.htm \| title=Image Synthesis — Noise Generation \| accessdate=11 October 2013 \| author=Robert Fisher, Simon Perkins, Ashley Walker, Erik Wolfart}}</ref>
			<ref name="Basel">{{cite web \| url=http://miac.unibas.ch/SIP/06-Restoration.html \| title=Image Restoration: Introduction to Signal and Image Processing \| publisher=MIAC, University of Basel \| date=2012-04-24 \| accessdate=11 October 2013 \| author=Dr. Philippe Cattin}}</ref>
			<ref name="Barbu">{{cite journal \| url=http://www.hindawi.com/journals/aaa/2013/856876/ \| title=Variational Image Denoising Approach with Diffusion Porous Media Flow \| author=Tudor Barbu \| journal=Abstract and Applied Analysis \| year=2013 \| volume=2013 \| pages=8 \| doi=10.1155/2013/856876}}</ref>
			}}

			{{physics-stub}}
			{{probability-stub}}

			{{DEFAULTSORT:Gaussian Noise}}

			[[Category:Noise]]
			[[Category:Stochastic processes]]
			[[Category:Time series analysis]]

en>Jacksonwalters: /* Preliminaries */ The condition that f(e)=1 follows from the fact that f is a homomorphism - it is not a separate condition.

2012-01-15T18:57:35Z

Preliminaries: The condition that f(e)=1 follows from the fact that f is a homomorphism - it is not a separate condition.

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Wilber Berryhill is the title his mothers and fathers gave him and he totally digs that name. What me and my family adore is doing ballet but I've been taking on new things recently. For many years he's been living in Mississippi and he doesn't strategy on altering it. Distributing manufacturing is how he makes a living.<br><br>My site [http://www.erozrywka.pl/users/GFHD love psychic readings]

Character group - Revision history

81.147.114.131 at 22:46, 6 July 2013

en>Jacksonwalters: /* Preliminaries */ The condition that f(e)=1 follows from the fact that f is a homomorphism - it is not a separate condition.