I have been using my noise function for more than four decades. For most of that time I thought of it as a tool for computer graphics. I’ve used it to create natural looking textures and shapes, to create realistic snow and fog, to make animated character movement appear more natural, or just to blow wind through the leaves in simulated trees.
But more recently I have been using it in quite a different way. As I implement various image analysis algorithms, I find that coherent noise is a very good stand-in for real-world data.
The danger with many analytic algorithms is that your approach might work really great with clean data that you cook up in the lab, yet fall apart when presented with the messiness of the real world.
By adding noise to my test data, I am able to shake things up, and make things more difficult for my algorithms. The result is that my code ends up being more robust and less prone to failure.
It’s kind of like what happens when you get a vaccine shot. Whenever you are exposed to a little bit of something that can harm you, your body starts to develop defenses.
I guess you could say that whatever doesn’t kill your algorithm makes it stronger.