Wavelet noise

Wavelet noise is an alternative to Perlin noise which reduces the problems of aliasing and detail loss that are encountered when Perlin noise is summed into a fractal.

Algorithm detail

The basic algorithm for 2-dimensional wavelet noise is as follows:

Create an image, $R$ , filled with uniform white noise.
Downsample $R$ to half-size to create $R^{\downarrow }$ , then upsample it back up to full size to create $R^{\downarrow \uparrow }$ .
Subtract $R^{\downarrow \uparrow }$ from $R$ to create the end result, $N$ .

This results in an image that contains all the information that cannot be represented at half-scale. From here, $N$ can be used similarly to Perlin noise to create fractal patterns.

External links

Wavelet Noise Paper at pixar.com.

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