glatning

Glatning is a term used in Norwegian to describe smoothing processes that aim to reduce rapid fluctuations or roughness in data, signals, images, or surfaces. The goal is to reveal underlying trends or structures while suppressing noise or irregular detail.

Across disciplines, glatning encompasses a variety of methods. In statistics and data analysis, common techniques include moving averages, kernel smoothing (such as Gaussian kernels), LOESS/LOWESS, and spline smoothing. The strength of smoothing is controlled by parameters like bandwidth, span, or smoothing degree, which are often selected by cross-validation or information criteria to balance bias and variance.

In signal processing and image processing, smoothing filters reduce noise and artifacts. Examples include Gaussian blur, median filters, anisotropic diffusion, and bilateral filtering. In computer graphics and geometric modeling, mesh smoothing methods such as Laplacian smoothing or Taubin smoothing reduce high-frequency geometry while attempting to preserve overall shape and avoid excessive shrinkage.

Glatning can also refer to spatial smoothing in geoscience and environmental statistics, where nearby observations are combined to produce a cleaner estimate of a field.

Applications include data preprocessing for modeling, denoising audio or images, or stabilizing numerical simulations. However, smoothing may blur or erase important features, so parameter choice and validation are important. Metrics for evaluation include residual analysis, cross-validated predictive performance, or visual inspection for feature preservation.