How the order parameter (n) in the Butterworth filter mathematically bridges the gap between the Ideal and Gaussian filters?

To understand this, let's look at the formula for the Butterworth Lowpass Filter (BLPF):

H(u,v)=11+[D(u,v)/D0]2n

The magic of the Butterworth filter lies entirely in that exponent, 2n. By simply changing the value of n, we fundamentally alter the geometry of the filter's shape in the frequency domain.

How the Order (n) Changes the Shape

Imagine looking at a 1-D cross-section (a side profile) of the filter. Here is what happens as we change n:

  1. Low Order (n=1) - The Gaussian Twin:

    When n=1, the transition from the passband (allowing frequencies, where H1) to the stopband (blocking frequencies, where H0) is very slow and gradual. At this low order, the Butterworth curve behaves almost exactly like a Gaussian filter. Because the transition is so smooth, it produces absolutely no "ringing" artifacts in the final image.

  2. Moderate Order (n=2 or 3) - The "Sweet Spot":

    As n increases to 2, the "slope" of the cutoff becomes a bit steeper. It holds onto the low frequencies a little better than a Gaussian, but drops off faster once it hits D0. A slight amount of ringing might technically be present, but it is usually completely imperceptible to the human eye. This is why n=2 is the standard default for image processing.

  3. High Order (n) - The Ideal Shape:

    Consider what happens to the math when n becomes very large (e.g., n=20 or higher).

    • If D<D0 (inside the cutoff), the fraction D/D0 is less than 1. A number less than 1 raised to a massive power approaches 0. So, H=1/(1+0)=1.

    • If D>D0 (outside the cutoff), the fraction D/D0 is greater than 1. A number greater than 1 raised to a massive power approaches infinity. So, H=1/(1+)=0.

    This creates an immediate, vertical drop-off at D0. The filter abruptly snaps from 1 to 0. This is the exact definition of the Ideal filter, which creates severe ringing artifacts.