Blurs, noise and other effects.

Many familiar visual effects are neighbourhood operations: each output pixel is calculated from a small region around its input position. The apparent simplicity comes from a set of carefully chosen representations, transformations and physical assumptions working together.

Convolution

A weighted matrix slides across an image to blur, sharpen or detect edges.

This is one part of a longer chain: source image becomes kernel / noise becomes sampling becomes effect. The useful abstraction hides the physical work, but the underlying constraints still shape the software built above it.

Gaussian blur

Weights fall away smoothly from the centre, producing an efficient approximation of optical softness.

The implementation is full of compromises. Precision, speed, storage and energy rarely improve together, so practical systems choose the errors people are least likely to notice.

Noise

Structured randomness adds texture, variation and believable imperfection.

Once this layer is visible, familiar design conventions stop looking arbitrary. They are accumulated responses to the capabilities and limits of the machinery below.