guide to Perlin noise frequency
| x-frequency | y-frequency | |
| 0.005 | N/A | ![]() ![]() |
| 0.01 | N/A | ![]() ![]() |
| 0.02 | N/A | ![]() ![]() |
| 0.04 | N/A | ![]() ![]() |
| 0.08 | N/A | ![]() ![]() |
| 0.16 | N/A | ![]() ![]() |
| 0.32 | N/A | ![]() ![]() |
| 0.005 | 0.005 | ![]() ![]() |
| 0.01 | 0.01 | ![]() ![]() |
| 0.02 | 0.02 | ![]() ![]() |
| 0.04 | 0.04 | ![]() ![]() |
| 0.08 | 0.08 | ![]() ![]() |
| 0.16 | 0.16 | ![]() ![]() |
| 0.32 | 0.32 | ![]() ![]() |
| 0.02 | 0.16 | ![]() ![]() |
| 0.01 | 0.08 | ![]() ![]() |
[generated using 8 bits of randomness, using a phase of 0.0137; any non-zero phase should give similar results]
[amplitude = noise(i * step * freq + phase)]
The perceived frequency is frequency * step_size. These images were generated with step_size=1 (i.e. each pixel is one step apart). Of course, if your step size is A, divide the values in the table by A to get the same result.































