Image models are a fundamental component of mathematically-founded image processing algorithm. Nonlinear, adaptive, local image models have a significant similarity with methods for interpolating data in many-dimensional spaces. The applicability of these methods will be demonstrated by mathematical analysis and experimental application to natural color scenes. In particular, a modification of the method of radial basis functions will be evaluated and various techniques for determining radial basis centers will be compared: random choice, k-means optimal, and two iterative constructive techniques.
David R. Cok, David R. Cok,
"Data interpolation techniques applied to image modeling", Proc. SPIE 1657, Image Processing Algorithms and Techniques III, (19 May 1992); doi: 10.1117/12.58354; https://doi.org/10.1117/12.58354