Links on Perlin noise 2005

Note: This page is kept online because it still receives hits. Beware, though, that the information is old and partly outdated.

I have found the links below to be interesting, either as background or further information on topics covered in the course.
I recommended them all for reading, as they give better or at least alternative presentations of difficult topics in the book.

Ken Perlin's newest creation "simplex noise", with a Java reference implementation at the end
My own clarification of "simplex noise", with a much more readable and also faster Java implementation
Ken Perlin's "Improved noise" (more like the "classic" Perlin Noise, but faster and better looking)
A clear explanation of the math behind Perlin noise, by Matt Zucker (Removed my local mirror, better resources exist now fifteen years later)
An often quoted tutorial by Hugo Elias, although it is not about Perlin noise (Removed my local mirror, no good reason to preserve it)

Comments to the links above:

Ken Perlin presents his latest noise research in two recent articles. Neither of them have had any impact on the noise() implementations in most graphics software, which is sad, because the new versions are really a lot better looking, faster and/or more hardware friendly. Please check it out. My own explanation of Simplex Noise was a public attempt to make it more popular, but it has not made much difference yet.

Matt Zucker presents (at the end of his article) a way of making noise periodic, so that it repeats seamlessly over a given distance. That particular section should be disregarded. Contrary to the first part of his article, this is bad, even wrong. His method is prohibitively expensive, and it also gives fairly evident visual artefacts in the resulting image. The fact that the value and derivatives of a periodic function match up at the boundaries does not imply that the pattern looks the same over the entire interval. (Try it and you will see what I mean. At the edges, you have one single noise() instance, while towards the middle, you take a weighted average of two noise() instances. Statistically and visually, these are two different things. The proposed method works for white noise with a normal distribution, but Perlin noise is very far from that.)

The correct way of doing periodic noise is this: Instead of treating noise() as a fixed function, look at its implementation. In the standard implementation, the gradient directions are arbitrarily chosen to repeat after 256 integer steps. You can easily change this to any integer period, although powers of 2 will be most efficient to implement. So, no need at all to interpolate like Matt suggests, just change the way the gradients are picked for the lattice points! (Non-integer periods are tricky to implement, and should be avoided.)

Hugo Elias presents interesting notes on value noise, which as you know is very different from gradient noise. Perlin noise is a gradient noise, not a value noise. Apart from that, his article is fine and clearly written, so just replace every occurrence of the term "Perlin noise" with the correct term "low-pass filtered or interpolated value noise", and you will be OK.