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Perlin noise is a type of gradient noise developed by Ken Perlin in 1983. It has many uses, including but not limited to: procedurally generating terrain , applying pseudo-random changes to a variable, and assisting in the creation of image textures .
An artifact of some implementations of this noise is that the returned value at the lattice points is 0. Unlike the value noise, gradient noise has more energy in the high frequencies. The first known implementation of a gradient noise function was Perlin noise , credited to Ken Perlin , who published the description of it in 1985.
Equivalent pulse code modulation noise; ... Peak signal-to-noise ratio; Perlin noise; ... Quantization noise; Quantum 1/f noise; Radio noise source; Random noise ...
Noise in computer graphics refers to various pseudo-random functions used to create textures, including: Gradient noise, created by interpolation of a lattice of pseudorandom gradients Perlin noise, a type of gradient noise developed in 1983; Simplex noise, a method for constructing an n-dimensional noise function comparable to Perlin noise
Abstract composition in 3D generated with the OpenSimplex noise generation algorithm. OpenSimplex noise is an n-dimensional (up to 4D) gradient noise function that was developed in order to overcome the patent-related issues surrounding simplex noise, while likewise avoiding the visually-significant directional artifacts characteristic of Perlin noise.
Perlin noise was also created by Ken Perlin as a built-in function within KPL. Much of the MAGi/SynthaVision software was Fortran -based, with a Ratfor interface for the artists. In 1985 Josh Pines argued to use the Unix programming environment for any future software and production programming design.
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Simplex noise. Simplex noise is the result of an n-dimensional noise function comparable to Perlin noise ("classic" noise) but with fewer directional artifacts, in higher dimensions, and a lower computational overhead. Ken Perlin designed the algorithm in 2001 [1] to address the limitations of his classic noise function, especially in higher ...