gives an image with pseudorandom pixel values in the range 0 to max.


generates pseudorandom pixel values in the range min to max.


generates pixel values using a symbolic distribution dist.


generates a random image of the specified size.

Details and Options

  • RandomImage gives an image where pixel values are drawn from a specified distribution. The pixel values are not correlated spatially.
  • Random images can be used for creating synthetic noise images and image effects.
  • RandomImage by default gives an image of a real type.
  • RandomImage[] is equivalent to RandomImage[1,{150,150}].
  • RandomImage[] is equivalent to RandomImage[,{150,150}].
  • The size specification can be one of the following:
  • side2D image of size {side,side}
    {width,height}2D image size specification
    {width,depth,height}3D image size specification
  • The following options are supported:
  • ColorSpaceAutomaticcolor space of the output image
    ImageSizeAutomaticdisplay image size
    MagnificationAutomatichow to magnify the displayed image


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Basic Examples  (3)

Generate a random image:

Generate a random RGB image:

Generate a random 3D image:

Scope  (7)

Distribution  (5)

By default, pixel values range from 0 to 1:

Specify a range of pixel values:

Generate an image from pseudorandom values in the range 0 to :

A random image from a distribution:

Generate a 4-channel image using some multivariate distribution:

Size  (2)

By default, a 2D image is generated:

Specify the image dimensions for a square image:

Specify the image dimensions:

Specify the 3D image dimensions:

Options  (5)

ColorSpace  (4)

By default, no explicit color space is assumed:

Generate a "Grayscale" image:

Generate a random "RGB" image:

Generate an "HSB" image using some univariate distribution:

Generate a "LAB" image using a multivariate uniform distribution with specific bounds:

ImageSize  (1)

By default, the image is created with ImageSize->Automatic:

Specify the display size:

Applications  (4)

Additive noise:

Multiplicative noise:

Image of Laplacian noise:

RGB image simulating salt-and-pepper noise using a heavy-tail distribution:

Properties & Relations  (7)

Generate an image with constant intensity:

Use the specialized function ConstantImage instead:

Generate the random data using RandomReal:

Generate a random binary image using RandomInteger:

Generate the random data using RandomVariate:

In a uniformly random image, neighboring pixels are not correlated:

After local filtering, pixel values drawn from the original distribution are spatially correlated:

Create an image with pixels randomly chosen from a list of colors:

Create an image from colors randomly chosen from a convex hull defined by a list of colors:

Possible Issues  (1)

Out-of-range pixel values are rendered as black or white:

Use ImageAdjust to see the full range of values:

Neat Examples  (1)

Show the effect of various types of noise: original image, Poisson noise, uniform noise, Gaussian noise, Laplacian noise, salt-and-pepper noise:

Introduced in 2010
Updated in 2012