In signal processing, a filter removes unwanted parts of the signal, such as random noise, or extracts the useful parts of the signal, such as the components lying within a certain frequency range.
FIR filters suffer from both data and coefficient quantization; each has different effects. Double-precision accumulation inside the FIR filter structure greatly reduces the data quantization error.
Digital filters must be properly scaled to prevent overflow in fixed-point implementations. Scaling by the sum of the absolute value of the impulse response of a filter prevents overflow. However, this is sometimes too conservative in practice, so less stringent rules are often used.
The fundamental filters for 1D and 2D signals are introduced and examples are given in Processing.
Si illustrano i filtri fondamentali per i segnali 1D e 2D e si forniscono esempi in Processing.
