There are several different methods to smooth a noisy signal. In this post I compare three common smoothing methods, namely a median filter, a Gaussian filter, and a Radian Basis Function (RBF) smoothing. RBF is a powerful tool not only for the multivariate data smoothing, but also for the interpolation, regression, etc. The following figure shows the magnificent performance of RBF compared to the median and Gaussian filters. The synthetic data was modified with Gaussian noise. I have used the ‘quintic’ kernel in this example.
Comparison of the RBF smoothing with the median and Gaussian filtering in a one-dimensional example.
the core program is fairly easy as it is a built-in function in python:
from scipy.interpolate import RBF
rbf = RBF(x, y, function=’quintic’, smooth=0.1)
s = rbf(x)
Micropore 