## Digital simulation of two-dimensional random fields with arbitrary power spectra and non-Gaussian probability distribution functions

Publication: Research - peer-review › Journal article – Annual report year: 2012

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**Digital simulation of two-dimensional random fields with arbitrary power spectra and non-Gaussian probability distribution functions.** / Yura, Harold; Hanson, Steen Grüner.

Publication: Research - peer-review › Journal article – Annual report year: 2012

### Harvard

*Applied Optics*, vol 51, no. 10, pp. C77-C83., 10.1364/AO.51.000C77

### APA

*Applied Optics*,

*51*(10), C77-C83. 10.1364/AO.51.000C77

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### MLA

*Applied Optics*. 2012, 51(10). C77-C83. Available: 10.1364/AO.51.000C77

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### Bibtex

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### RIS

TY - JOUR

T1 - Digital simulation of two-dimensional random fields with arbitrary power spectra and non-Gaussian probability distribution functions

AU - Yura,Harold

AU - Hanson,Steen Grüner

N1 - This paper was published in Applied Optics and is made available as an electronic reprint with the permission of OSA. The paper can be found at the following URL on the OSA website: http://www.opticsinfobase.org/ao/abstract.cfm?uri=ao-51-10-C77. Systematic or multiple reproduction or distribution to multiple locations via electronic or other means is prohibited and is subject to penalties under law.

PY - 2012

Y1 - 2012

N2 - Methods for simulation of two-dimensional signals with arbitrary power spectral densities and signal amplitude probability density functions are disclosed. The method relies on initially transforming a white noise sample set of random Gaussian distributed numbers into a corresponding set with the desired spectral distribution, after which this colored Gaussian probability distribution is transformed via an inverse transform into the desired probability distribution. In most cases the method provides satisfactory results and can thus be considered an engineering approach. Several illustrative examples with relevance for optics are given.

AB - Methods for simulation of two-dimensional signals with arbitrary power spectral densities and signal amplitude probability density functions are disclosed. The method relies on initially transforming a white noise sample set of random Gaussian distributed numbers into a corresponding set with the desired spectral distribution, after which this colored Gaussian probability distribution is transformed via an inverse transform into the desired probability distribution. In most cases the method provides satisfactory results and can thus be considered an engineering approach. Several illustrative examples with relevance for optics are given.

U2 - 10.1364/AO.51.000C77

DO - 10.1364/AO.51.000C77

M3 - Journal article

VL - 51

SP - C77-C83

JO - Applied Optics

T2 - Applied Optics

JF - Applied Optics

SN - 1559-128X

IS - 10

ER -