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Add noise to image

`J = imnoise(I,`

* type*)

J = imnoise(I,

`type`

`parameters`

J = imnoise(I,'gaussian',M,V)

J = imnoise(I,'localvar',V)

J = imnoise(I,'localvar',image_intensity,var)

J = imnoise(I,'poisson')

J = imnoise(I,'salt & pepper',d)

J = imnoise(I,'speckle',v)

gpuarrayJ = imnoise(gpuarrayI,___)

`J = imnoise(I,`

adds
noise of a given type to the intensity image * type*)

`I`

. `type`

Value | Description |
---|---|

| Gaussian white noise with constant mean and variance |

| Zero-mean Gaussian white noise with an intensity-dependent variance |

| Poisson noise |

| On and off pixels |

| Multiplicative noise |

`J = imnoise(I,`

Depending on * type*,

`parameters`

`type`

, you can specify additional
parameters to `imnoise`

. All numerical parameters
are normalized— they correspond to operations with images with
intensities ranging from 0 to 1.`J = imnoise(I,'gaussian',M,V)`

adds Gaussian white noise of mean `m`

and variance `v`

to
the image `I`

. The default is zero mean noise with
0.01 variance.

`J = imnoise(I,'localvar',V)`

adds zero-mean, Gaussian white noise of local variance `V`

to
the image `I`

. `V`

is an array of
the same size as `I`

.

`J = imnoise(I,'localvar',image_intensity,var)`

adds zero-mean, Gaussian noise to an image `I`

,
where the local variance of the noise, `var`

, is
a function of the image intensity values in `I`

.
The `image_intensity`

and `var`

arguments
are vectors of the same size, and `plot(image_intensity,var)`

plots
the functional relationship between noise variance and image intensity.
The `image_intensity`

vector must contain normalized
intensity values ranging from 0 to 1.

`J = imnoise(I,'poisson')`

generates
Poisson noise from the data instead of adding artificial noise to
the data. If `I`

is double precision, then input
pixel values are interpreted as means of Poisson distributions scaled
up by `1e12`

. For example, if an input pixel has
the value `5.5e-12`

, then the corresponding output
pixel will be generated from a Poisson distribution with mean of 5.5
and then scaled back down by `1e12`

. If `I`

is
single precision, the scale factor used is `1e6`

.
If `I`

is `uint8`

or `uint16`

,
then input pixel values are used directly without scaling. For example,
if a pixel in a `uint8`

input has the value 10, then
the corresponding output pixel will be generated from a Poisson distribution
with mean 10.

`J = imnoise(I,'salt & pepper',d)`

adds
salt and pepper noise to the image I, where `d`

is
the noise density. This affects approximately `d*numel(I)`

pixels.
The default for `d`

is 0.05.

`J = imnoise(I,'speckle',v)`

adds
multiplicative noise to the image `I`

, using the
equation `J = I+n*I`

, where `n`

is
uniformly distributed random noise with mean 0 and variance `v`

.
The default for `v`

is 0.04.

The mean and variance parameters for `'gaussian'`

, `'localvar'`

,
and `'speckle'`

noise types are always specified
as if the image were of class `double`

in the range
[0, 1]. If the input image is of class `uint8`

or `uint16`

,
the `imnoise`

function converts the image to `double`

,
adds noise according to the specified type and parameters, and then
converts the noisy image back to the same class as the input.

`gpuarrayJ = imnoise(gpuarrayI,___)`

adds
noise to the gpuArray intensity image `gpuarrayI`

,
performing the operation on a GPU. Returns a gpuArray image `J`

of
the same class. This syntax requires the Parallel
Computing Toolbox™.

For most noise types, the input image `I`

can
be of class `uint8`

, `uint16`

, `int16`

, `single`

,
or `double`

. For Poisson noise, `int16`

is
not allowed. The output image `J`

is of the same
class as `I`

. If `I`

has more than
two dimensions it is treated as a multidimensional intensity image
and not as an RGB image.

An input gpuArray image `I`

can be of class `uint8`

, `uint16`

, `int16`

, `single`

,
or `double`

. For Poisson noise, `int16`

is
not allowed. The output gpuArray image `J`

is of
the same class as `I`

. If `I`

has
more than two dimensions it is treated as a multidimensional intensity
image and not as an RGB gpuArray image.

Add noise to an image.

I = imread('eight.tif'); J = imnoise(I,'salt & pepper',0.02); figure, imshow(I)

figure, imshow(J)

I = gpuArray(imread('eight.tif')); J = imnoise(I,'salt & pepper', 0.02); figure, imshow(I); figure, imshow(J);

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