Data sets often contain noise that hinders their processing and analysis. When the nature of the noise is unknown, it is difficult to distinguish between noise and actual data features.
We propose a smoothing method for 2D scalar fields that gives explicit control over the data features, i.e., critical points and the topological structure they induce. Feature significance is rated according to topological persistence.
This is the first topological smoothing method that guarantees a C1-continuous output scalar field with the exact specified features and topological structures.