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
specifies
any of the following types of noise. Note that certain types of noise
support additional parameters. See the related syntax.
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.
adds zero-mean, Gaussian white noise of local variance J = imnoise(I,'localvar',V)
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.
Note
The mean and variance parameters for |
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);