  Cloudiness or formation is a concept routinely used in industry to address
deviations from homogeneity in nonwovens and papers. Measuring a cloudiness
index based on image data is a common task in industrial quality assurance. The
two most popular ways of quantifying cloudiness are based on power spectrum or
correlation function on the one hand or the Laplacian pyramid on the other
hand. Here, we recall the mathematical basis of the first approach
comprehensively, derive a cloudiness index, and demonstrate its practical
estimation. We prove that the Laplacian pyramid as well as other quantities
characterizing cloudiness like the range of interaction and the intensity of
small-angle scattering are very closely related to the power spectrum. Finally,
we show that the power spectrum is easy to be measured image analytically and
carries more information than the alternatives.
