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White and Gaussian noise

Random-noise generators choose sample values from a distribution. The spatial relationship between samples and the value distribution are separate properties.

Connect random samples to their measured histogram

1,024 independent seeded samples
One random draw per cell32 × 32 · seed 42
Observed brightness distribution00.5189
Where do the values land?16 brightness bins · vertical axis: sample count

Every value between 0 and 1 is equally likely. A finite sample produces an uneven histogram.

Observed mean0.527Clipped draws0 / 1024

Try this Choose Gaussian and reduce σ: the histogram narrows while neighboring cells remain independent. Raise σ to see clipped values collect at 0 and 1.

Compare named distributions, then narrow and widen Gaussian draws while keeping the seed fixed. Use any control to take over.

What to try
  1. Compare Uniform and Gaussian distributions.
  2. For Gaussian, vary mean and standard deviation; draw a new seed and restore seed 42.
Open in the Lab ↗

What to look for: The histogram counts the displayed samples. Narrow deviation concentrates brightness, while extreme Gaussian samples are clipped to 0 or 1. Restoring the seed reproduces the same draws.

How it works

The Lab’s white-noise generator uses a uniform value distribution for independent cells. The Gaussian generator concentrates values around a mean according to a standard deviation. Both are seeded, so the same settings and dimensions reproduce the same realization.

Cell size controls the spatial footprint of each draw. A larger size repeats a value over a bigger area, making a mosaic rather than fine grain. Mapping low and high values to colors changes the appearance without changing which random draws occurred.

Use it in the Lab

Use noise as a starting material for thresholding, blur, morphology or grain-like layers. For coherent shapes rather than independent values, use Perlin or another spatially correlated field.

Limits & distinctions

The output is bounded to the display range, so extreme Gaussian values are clipped. A uniform distribution and a white spectrum are different concepts even though both apply to this independent uniform generator.

Sources & references

Guide updated