1.1.1.0 image.addnoise

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image.addnoise - Function

1.1.1 Add noise to the image
Description

This function adds noise to the image. You may zero the image first before the noise is added if you wish.

The noise can be drawn from one of many distributions.

For each distribution, you must supply the type via the type argument (minimum match is active) and parameters via the pars argument. Briefly:

binomial – the binomial distribution models successfully drawing items from a pool. Specify two parameters, n and p, respectively. n is the number of items in the pool, and p, is the probability of each item being successfully drawn. It is required that n > 0 and 0 ≤ p ≤ 1.
discreteuniform – models a uniform random variable over the closed interval. Specify two parameters, the low and high values, respectively. The low parameter is the lowest possible return value and the high parameter is the highest. It is required that low < high.
erlang – Specify two parameters, the mean and variance, respectively. It is required that the mean is non-zero and the variance is positive.
geometric – Specify one parameter, the probability. It is required that 0 ≤ probability < 1.
hypergeometric – Specify two parameters, the mean and the variance. It is required that the variance is positive and that the mean is non-zero and not bigger than the square-root of the variance.
normal – Specify two parameters, the mean and the variance. It is required that the variance is positive.
lognormal – Specify two parameters, the mean and the variance. It is required that the supplied variance is positive and that the mean is non-zero.
negativeexponential – Supply one parameter, the mean.
poisson – Specify one parameter, the mean. It is required that the mean is non-negative.
uniform – Model a uniform random variable over a closed interval. Specify two parameters, the low and high values. The low parameter is the lowest possible return value and the high parameter can never be returned. It is required that low < high.
weibull – Specify two parameters, alpha and beta. It is required that the alpha parameter is not zero.

Arguments


Inputs
type	Type of distribution, normal
	allowed:	string
	Default:	normal
pars	Parameters of distribution
	allowed:	doubleArray
	Default:	0.0 1.0
region	Region of interest. Default is whole image.
	allowed:	any
	Default:	record
zero	Zero image first?
	allowed:	bool
	Default:	false

Returns
bool

Example

"""
#
print "\t----\t addnoise Ex 1 \t----"
ia.maketestimage() # im1 = maketestimage()
ia.addnoise(type=’normal’, pars=[0.5, 1], zero=T)
ia.statistics()
ia.close()
#
"""

A test image is created, zeroed, and noise of mean 0.5 and variance 1 from a normal distribution added.

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