sfsmooth, one of the most useful Madagascar programs, implements smoothing by triangle filtering.

The idea of triangle filtering is explained in Jon Claerbout’s books *Processing versus Inversion* and *Image Estimation by Example*. You can find the explanation in the chapter Smoothing with box and triangle. The triangle filter of radius is a correlation of two box filters. In the *Z*-transform notation,

$$T_k(Z) = B_k(1/Z)\,B_k(Z)$$

where

$$B_k(Z) = \displaystyle \frac{1}{k}\,\left(1+Z+Z^2+\cdots+Z^k\right) = \frac{1}{k}\,\frac{1-Z^{k+1}}{1-Z}$$

and $k$ is the smoothing radius. Triangle filtering is a very efficient operation, because it requires only $N$ multiplications for the input of size $N$.

Triangle filtering serves as a good approximation to more expensive Gaussian filtering. Repeated triangle filtering rapidly approaches Gaussian filtering (a consequence of the central limit theorem). The following example from jsg/shape/smoo demonstrates how repeated application of triangle smoothing turns a triangle filter into a Gaussian filter

The following example from gee/ajt/triangle demonstrated how **sfsmooth** handles boundary conditions: the energy is reflected from the side in order to preserve the zero-frequency (DC) component of the input signal. Repeated smoothing or smoothing with a very large radius turns every signal into a constant.

In multiple dimensions, triangle smoothing is applied sequentially in different directions, possible with different radii (controlled by **rect#=** ) parameter. The following example from gee/ajt/mound shows shapes created by 2-D triangle smoothing after one pass and two passes (**repeat=2**).

Triangle smoothing is theoretically a self-adjoint operator. Numerically, the adjoint and forward operators are different. The action is controlled by the **adj=** parameter.

For smoothing with a box instead of a triangle, use sfboxsmooth.

For a different approach to smoothing, try sfgaussmooth, which implements smoothing by recursive Gaussian filtering.

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