PROBABILISTIC LINEAR-MODELS FOR MULTIRESOLUTION ESTIMATION IN GRAY-LEVEL IMAGES

被引：11

作者：

MARTINEZAROZA, J ^{[1
]}

ROMANROLDAN, R ^{[1
]}

机构：

[1] UNIV GRANADA,DEPT APPL PHYS,E-18071 GRANADA,SPAIN

来源：

MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING | 1995年 / 6卷 / 01期

关键词：

GRAY-LEVEL IMAGE; HISTOGRAM ESTIMATION; PROBABILISTIC MODELS FOR IMAGES; MULTIRESOLUTION; IMAGE PROCESSING; NONLINEAR FILTER;

D O I：

10.1007/BF00980143

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A multiresolution analysis of digital gray-lever images is presented. A gray-level multi-scale framework is determined from two main assumptions: the gray scale is binary at the finest spatial resolution, and the gray levels of composed regions are obtained additively. In order to interrelate the gray-level histograms of the same image at different resolutions, probabilistic linear models are developed, which are then applied for estimation. Linear-optimization theory is used as a way of constructing such models. A general procedure for image processing is sketched, based on gray-level estimation. A versatile algorithm for nonlinear filtering is derived. Some examples of prospective applications are given.

引用

页码：7 / 35

页数：29

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