← indexYou've most likely heard about gradient noise through the name Perlin noise, which refers to one particular implementation with various CPU optimizations. A welcoming wavy 1D gradient noise signalHashing function and pseudo-random valuesAt the most elementary level, we need a deterministic coordinate based pseudo-random system. So instead of using the random as noise values directly, we interpret them as gradient: this is the gradient noise. For each lattice, we decide to give them y=0 at regular interval, and have their random gradient impacts the surrounding curve. Note One important property of the gradient noise is that it passes through 0 at every lattice, meaning at a constant rate.