PixInsight 1.5

The Officially Unofficial Reference Guide

Rev.0.1 – 3/29/2010

Section 14: NoiseGeneration


NoiseGenerator is the standard noise generation tool in PixInsight. It is based on the most advanced uniform noise generator algorithm available today.

Amount: Define the strength in which the noise effect will be applied.


Uniform: Uniform noise rarely occurs in nature but when digitizing a signal, errors take place that are uniformly distributed. The noise variance is independent of the image intensity.

Normal: In nature nearly everything is normally distributed. Normally distributed noise is Gaussian noise. Like Uniform noise, the noise variance is independent of the image intensity.

Poisson: A source for Poisson noise is photon counting. When taking a picture in the real world, photons arrive at a certain rate. However photons are not correlated and thus the time between photons is not always the same. The number of photons you actually collect is Poisson distributed.

Impulsional (Salt & Pepper): The distribution of impulsional noise decays very slowly. This is a so called fat-tail distribution and causes the salt and pepper noise effect.

Probability: This parameter is only active when the Impulsional distribution is selected. Noise is characterized by a probability distribution function (PDF). The higher this value, the more probability of noise being generated.

Preserve Median: Tends to preserve median values for individual nominal channels.

Preserve Mean: Tends to preserve average values for individual nominal channels.


SimplexNoise is PixInsight's implementation of the simplex noise algorithm created by Ken Perlin. Simple noise is an enhanced version of the classical Perlin noise algorithm, which is a fundamental tool for generation of textures and realistic simulations.

Noise generated by the SimplexNoise algorithm is not random noise but precalculated, simulated noise.

Amount: Define the strength in which the noise effect will be applied.

Scale: Scale of the noise.

X/Y Offsets: Indicate the offset into the noise pattern. SimplexNoise defines a huge precalculated pattern. With this parameter, you define the position (offset) in this huge noise pattern field so that the pattern applied to your image will be different depending on the offset values.

Operator: Once the noise pattern has been generated, you can choose different ways to add it to the image. The Copy operator simply takes the noise pattern and places it over the image with complete disregard for whatever data was present there before. All other options should be self-explanatory, as they all are straight operators, except for the screen operator which applies a 1 - (1-Source) × (1-Noise) operation.

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