Two-dimensional variation and image decomposition
Pavel A. Chochia, Olga P. Miliukova · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
An image is assumed to be a blend of several statistically and semantically independent components, containing details of different information classes. Discussing about image decomposition we mean the splitting of an image onto the set of such components. Statistical distinctions of them allow to find effective algorithm for decomposition an image to several components of different properties. It gives the opportunity to extract form an image only the component of interest, thus to avoid redundant information from the following analysis, and finally to create decomposition- based image processing methods. To compare resulting images we introduce some formal quantitative measure for image estimation 2D variation, which is relied on continual image model. Its application for estimating of image decomposition is discussed. Under investigation we consider discrete model of image fragment, find rank algorithm for image decomposition, and discuss estimates of 2D variation.